text: """Python wrappers around TensorFlow ops.
  
  This file is MACHINE GENERATED! Do not edit.
  Original C++ source file: math_ops.cc
  """
  
  import collections
  
  from tensorflow.python import pywrap_tfe as pywrap_tfe
  from tensorflow.python.eager import context as _context
  from tensorflow.python.eager import core as _core
  from tensorflow.python.eager import execute as _execute
  from tensorflow.python.framework import dtypes as _dtypes
  
  from tensorflow.python.framework import op_def_registry as _op_def_registry
  from tensorflow.python.framework import ops as _ops
  from tensorflow.python.framework import op_def_library as _op_def_library
  from tensorflow.python.util.deprecation import deprecated_endpoints
  from tensorflow.python.util import dispatch as _dispatch
  from tensorflow.python.util.tf_export import tf_export
  
  
  def _abs(x, name=None):
    r"""Computes the absolute value of a tensor.
  
    Given a tensor `x`, this operation returns a tensor containing the absolute
    value of each element in `x`. For example, if x is an input element and y is
    an output element, this operation computes \\(y = |x|\\).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Abs", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _abs_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Abs", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Abs", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Abs = tf_export("raw_ops.Abs")(_ops.to_raw_op(_abs))
  
  
  def _abs_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Abs", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Abs", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def accumulate_nv2(inputs, shape, name=None):
    r"""Returns the element-wise sum of a list of tensors.
  
    `tf.accumulate_n_v2` performs the same operation as `tf.add_n`, but does not
    wait for all of its inputs to be ready before beginning to sum. This can
    save memory if inputs are ready at different times, since minimum temporary
    storage is proportional to the output size rather than the inputs size.
  
    Unlike the original `accumulate_n`, `accumulate_n_v2` is differentiable.
  
    Returns a `Tensor` of same shape and type as the elements of `inputs`.
  
    Args:
      inputs: A list of at least 1 `Tensor` objects with the same type in: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        A list of `Tensor` objects, each with same shape and type.
      shape: A `tf.TensorShape` or list of `ints`.
        Shape of elements of `inputs`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `inputs`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "AccumulateNV2", name,
          tld.op_callbacks, inputs, "shape", shape)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return accumulate_nv2_eager_fallback(
            inputs, shape=shape, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if not isinstance(inputs, (list, tuple)):
      raise TypeError(
          "Expected list for 'inputs' argument to "
          "'accumulate_nv2' Op, not %r." % inputs)
    _attr_N = len(inputs)
    shape = _execute.make_shape(shape, "shape")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "AccumulateNV2", inputs=inputs, shape=shape, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("N", _op._get_attr_int("N"), "T", _op._get_attr_type("T"),
                "shape", _op.get_attr("shape"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "AccumulateNV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  AccumulateNV2 = tf_export("raw_ops.AccumulateNV2")(_ops.to_raw_op(accumulate_nv2))
  
  
  def accumulate_nv2_eager_fallback(inputs, shape, name, ctx):
    if not isinstance(inputs, (list, tuple)):
      raise TypeError(
          "Expected list for 'inputs' argument to "
          "'accumulate_nv2' Op, not %r." % inputs)
    _attr_N = len(inputs)
    shape = _execute.make_shape(shape, "shape")
    _attr_T, inputs = _execute.args_to_matching_eager(list(inputs), ctx)
    _inputs_flat = list(inputs)
    _attrs = ("N", _attr_N, "T", _attr_T, "shape", shape)
    _result = _execute.execute(b"AccumulateNV2", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "AccumulateNV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.acos', 'acos')
  def acos(x, name=None):
    r"""Computes acos of x element-wise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Acos", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return acos_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              acos, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Acos", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            acos, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Acos", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Acos = tf_export("raw_ops.Acos")(_ops.to_raw_op(acos))
  
  
  def acos_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Acos", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Acos", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.acosh', 'acosh')
  def acosh(x, name=None):
    r"""Computes inverse hyperbolic cosine of x element-wise.
  
    Given an input tensor, the function computes inverse hyperbolic cosine of every element.
    Input range is `[1, inf]`. It returns `nan` if the input lies outside the range.
  
    ```python
    x = tf.constant([-2, -0.5, 1, 1.2, 200, 10000, float("inf")])
    tf.math.acosh(x) ==> [nan nan 0. 0.62236255 5.9914584 9.903487 inf]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Acosh", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return acosh_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              acosh, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Acosh", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            acosh, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Acosh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Acosh = tf_export("raw_ops.Acosh")(_ops.to_raw_op(acosh))
  
  
  def acosh_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Acosh", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Acosh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.add', 'add')
  def add(x, y, name=None):
    r"""Returns x + y element-wise.
  
    *NOTE*: `math.add` supports broadcasting. `AddN` does not. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`, `string`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Add", name, tld.op_callbacks,
          x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return add_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              add, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Add", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            add, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Add", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Add = tf_export("raw_ops.Add")(_ops.to_raw_op(add))
  
  
  def add_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Add", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Add", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def add_n(inputs, name=None):
    r"""Add all input tensors element wise.
  
      Inputs must be of same size and shape.
  
      ```python
      x = [9, 7, 10]
      tf.math.add_n(x) ==> 26
      ```
  
    Args:
      inputs: A list of at least 1 `Tensor` objects with the same type in: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `variant`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `inputs`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "AddN", name, tld.op_callbacks,
          inputs)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return add_n_eager_fallback(
            inputs, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if not isinstance(inputs, (list, tuple)):
      raise TypeError(
          "Expected list for 'inputs' argument to "
          "'add_n' Op, not %r." % inputs)
    _attr_N = len(inputs)
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "AddN", inputs=inputs, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("N", _op._get_attr_int("N"), "T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "AddN", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  AddN = tf_export("raw_ops.AddN")(_ops.to_raw_op(add_n))
  
  
  def add_n_eager_fallback(inputs, name, ctx):
    if not isinstance(inputs, (list, tuple)):
      raise TypeError(
          "Expected list for 'inputs' argument to "
          "'add_n' Op, not %r." % inputs)
    _attr_N = len(inputs)
    _attr_T, inputs = _execute.args_to_matching_eager(list(inputs), ctx)
    _inputs_flat = list(inputs)
    _attrs = ("N", _attr_N, "T", _attr_T)
    _result = _execute.execute(b"AddN", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "AddN", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def add_v2(x, y, name=None):
    r"""Returns x + y element-wise.
  
    *NOTE*: `Add` supports broadcasting. `AddN` does not. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "AddV2", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return add_v2_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "AddV2", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "AddV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  AddV2 = tf_export("raw_ops.AddV2")(_ops.to_raw_op(add_v2))
  
  
  def add_v2_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"AddV2", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "AddV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _all(input, axis, keep_dims=False, name=None):
    r"""Computes the "logical and" of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor` of type `bool`. The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "All", name, tld.op_callbacks,
          input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _all_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "All", input=input, reduction_indices=axis, keep_dims=keep_dims,
                 name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "Tidx",
                _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "All", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  All = tf_export("raw_ops.All")(_ops.to_raw_op(_all))
  
  
  def _all_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    input = _ops.convert_to_tensor(input, _dtypes.bool)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"All", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "All", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def angle(input, Tout=_dtypes.float32, name=None):
    r"""Returns the argument of a complex number.
  
    Given a tensor `input` of complex numbers, this operation returns a tensor of
    type `float` that is the argument of each element in `input`. All elements in
    `input` must be complex numbers of the form \\(a + bj\\), where *a*
    is the real part and *b* is the imaginary part.
  
    The argument returned by this operation is of the form \\(atan2(b, a)\\).
  
    For example:
  
    ```
    # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
    tf.angle(input) ==> [2.0132, 1.056]
    ```
  
    @compatibility(numpy)
    Equivalent to np.angle.
    @end_compatibility
  
    Args:
      input: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
      Tout: An optional `tf.DType` from: `tf.float32, tf.float64`. Defaults to `tf.float32`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `Tout`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Angle", name,
          tld.op_callbacks, input, "Tout", Tout)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return angle_eager_fallback(
            input, Tout=Tout, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Angle", input=input, Tout=Tout, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tout",
                _op._get_attr_type("Tout"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Angle", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Angle = tf_export("raw_ops.Angle")(_ops.to_raw_op(angle))
  
  
  def angle_eager_fallback(input, Tout, name, ctx):
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, _dtypes.complex64)
    _inputs_flat = [input]
    _attrs = ("T", _attr_T, "Tout", Tout)
    _result = _execute.execute(b"Angle", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Angle", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _any(input, axis, keep_dims=False, name=None):
    r"""Computes the "logical or" of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor` of type `bool`. The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Any", name, tld.op_callbacks,
          input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _any_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Any", input=input, reduction_indices=axis, keep_dims=keep_dims,
                 name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "Tidx",
                _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Any", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Any = tf_export("raw_ops.Any")(_ops.to_raw_op(_any))
  
  
  def _any_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    input = _ops.convert_to_tensor(input, _dtypes.bool)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"Any", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Any", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def approximate_equal(x, y, tolerance=1e-05, name=None):
    r"""Returns the truth value of abs(x-y) < tolerance element-wise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
      y: A `Tensor`. Must have the same type as `x`.
      tolerance: An optional `float`. Defaults to `1e-05`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "ApproximateEqual", name,
          tld.op_callbacks, x, y, "tolerance", tolerance)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return approximate_equal_eager_fallback(
            x, y, tolerance=tolerance, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if tolerance is None:
      tolerance = 1e-05
    tolerance = _execute.make_float(tolerance, "tolerance")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "ApproximateEqual", x=x, y=y, tolerance=tolerance, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "tolerance",
                _op.get_attr("tolerance"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "ApproximateEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  ApproximateEqual = tf_export("raw_ops.ApproximateEqual")(_ops.to_raw_op(approximate_equal))
  
  
  def approximate_equal_eager_fallback(x, y, tolerance, name, ctx):
    if tolerance is None:
      tolerance = 1e-05
    tolerance = _execute.make_float(tolerance, "tolerance")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T, "tolerance", tolerance)
    _result = _execute.execute(b"ApproximateEqual", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "ApproximateEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def arg_max(input, dimension, output_type=_dtypes.int64, name=None):
    r"""Returns the index with the largest value across dimensions of a tensor.
  
    Note that in case of ties the identity of the return value is not guaranteed.
  
    Usage:
      ```python
      import tensorflow as tf
      a = [1, 10, 26.9, 2.8, 166.32, 62.3]
      b = tf.math.argmax(input = a)
      c = tf.keras.backend.eval(b)
      # c = 4
      # here a[4] = 166.32 which is the largest element of a across axis 0
      ```
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
      dimension: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        int32 or int64, must be in the range `[-rank(input), rank(input))`.
        Describes which dimension of the input Tensor to reduce across. For vectors,
        use dimension = 0.
      output_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `output_type`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "ArgMax", name,
          tld.op_callbacks, input, dimension, "output_type", output_type)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return arg_max_eager_fallback(
            input, dimension, output_type=output_type, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if output_type is None:
      output_type = _dtypes.int64
    output_type = _execute.make_type(output_type, "output_type")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "ArgMax", input=input, dimension=dimension, output_type=output_type,
                    name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "output_type",
                _op._get_attr_type("output_type"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "ArgMax", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  ArgMax = tf_export("raw_ops.ArgMax")(_ops.to_raw_op(arg_max))
  
  
  def arg_max_eager_fallback(input, dimension, output_type, name, ctx):
    if output_type is None:
      output_type = _dtypes.int64
    output_type = _execute.make_type(output_type, "output_type")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (dimension,) = _execute.args_to_matching_eager([dimension], ctx, _dtypes.int32)
    _inputs_flat = [input, dimension]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "output_type", output_type)
    _result = _execute.execute(b"ArgMax", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "ArgMax", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def arg_min(input, dimension, output_type=_dtypes.int64, name=None):
    r"""Returns the index with the smallest value across dimensions of a tensor.
  
    Note that in case of ties the identity of the return value is not guaranteed.
  
    Usage:
      ```python
      import tensorflow as tf
      a = [1, 10, 26.9, 2.8, 166.32, 62.3]
      b = tf.math.argmin(input = a)
      c = tf.keras.backend.eval(b)
      # c = 0
      # here a[0] = 1 which is the smallest element of a across axis 0
      ```
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`, `bool`.
      dimension: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        int32 or int64, must be in the range `[-rank(input), rank(input))`.
        Describes which dimension of the input Tensor to reduce across. For vectors,
        use dimension = 0.
      output_type: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `output_type`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "ArgMin", name,
          tld.op_callbacks, input, dimension, "output_type", output_type)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return arg_min_eager_fallback(
            input, dimension, output_type=output_type, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if output_type is None:
      output_type = _dtypes.int64
    output_type = _execute.make_type(output_type, "output_type")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "ArgMin", input=input, dimension=dimension, output_type=output_type,
                    name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "output_type",
                _op._get_attr_type("output_type"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "ArgMin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  ArgMin = tf_export("raw_ops.ArgMin")(_ops.to_raw_op(arg_min))
  
  
  def arg_min_eager_fallback(input, dimension, output_type, name, ctx):
    if output_type is None:
      output_type = _dtypes.int64
    output_type = _execute.make_type(output_type, "output_type")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (dimension,) = _execute.args_to_matching_eager([dimension], ctx, _dtypes.int32)
    _inputs_flat = [input, dimension]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "output_type", output_type)
    _result = _execute.execute(b"ArgMin", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "ArgMin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.asin', 'asin')
  def asin(x, name=None):
    r"""Computes the trignometric inverse sine of x element-wise.
  
    The `tf.math.asin` operation returns the inverse of `tf.math.sin`, such that
    if `y = tf.math.sin(x)` then, `x = tf.math.asin(y)`.
  
    **Note**: The output of `tf.math.asin` will lie within the invertible range
    of sine, i.e [-pi/2, pi/2].
  
    For example:
  
    ```python
    # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]
    x = tf.constant([1.047, 0.785])
    y = tf.math.sin(x) # [0.8659266, 0.7068252]
  
    tf.math.asin(y) # [1.047, 0.785] = x
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Asin", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return asin_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              asin, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Asin", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            asin, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Asin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Asin = tf_export("raw_ops.Asin")(_ops.to_raw_op(asin))
  
  
  def asin_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Asin", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Asin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.asinh', 'asinh')
  def asinh(x, name=None):
    r"""Computes inverse hyperbolic sine of x element-wise.
  
      Given an input tensor, this function computes inverse hyperbolic sine
      for every element in the tensor. Both input and output has a range of
      `[-inf, inf]`.
  
      ```python
      x = tf.constant([-float("inf"), -2, -0.5, 1, 1.2, 200, 10000, float("inf")])
      tf.math.asinh(x) ==> [-inf -1.4436355 -0.4812118 0.8813736 1.0159732 5.991471 9.903487 inf]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Asinh", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return asinh_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              asinh, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Asinh", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            asinh, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Asinh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Asinh = tf_export("raw_ops.Asinh")(_ops.to_raw_op(asinh))
  
  
  def asinh_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Asinh", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Asinh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.atan', 'atan')
  def atan(x, name=None):
    r"""Computes the trignometric inverse tangent of x element-wise.
  
    The `tf.math.atan` operation returns the inverse of `tf.math.tan`, such that
    if `y = tf.math.tan(x)` then, `x = tf.math.atan(y)`.
  
    **Note**: The output of `tf.math.atan` will lie within the invertible range
    of tan, i.e (-pi/2, pi/2).
  
    For example:
  
    ```python
    # Note: [1.047, 0.785] ~= [(pi/3), (pi/4)]
    x = tf.constant([1.047, 0.785])
    y = tf.math.tan(x) # [1.731261, 0.99920404]
  
    tf.math.atan(y) # [1.047, 0.785] = x
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Atan", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return atan_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              atan, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Atan", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            atan, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Atan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Atan = tf_export("raw_ops.Atan")(_ops.to_raw_op(atan))
  
  
  def atan_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Atan", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Atan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.atan2', 'atan2')
  def atan2(y, x, name=None):
    r"""Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
  
    This is the angle \( \theta \in [-\pi, \pi] \) such that
    \[ x = r \cos(\theta) \]
    and
    \[ y = r \sin(\theta) \]
    where \(r = \sqrt(x^2 + y^2) \).
  
    Args:
      y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      x: A `Tensor`. Must have the same type as `y`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `y`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Atan2", name,
          tld.op_callbacks, y, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return atan2_eager_fallback(
            y, x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              atan2, (), dict(y=y, x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Atan2", y=y, x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            atan2, (), dict(y=y, x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Atan2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Atan2 = tf_export("raw_ops.Atan2")(_ops.to_raw_op(atan2))
  
  
  def atan2_eager_fallback(y, x, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([y, x], ctx)
    (y, x) = _inputs_T
    _inputs_flat = [y, x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Atan2", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Atan2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.atanh', 'atanh')
  def atanh(x, name=None):
    r"""Computes inverse hyperbolic tangent of x element-wise.
  
      Given an input tensor, this function computes inverse hyperbolic tangent
      for every element in the tensor. Input range is `[-1,1]` and output range is
      `[-inf, inf]`. If input is `-1`, output will be `-inf` and if the
      input is `1`, output will be `inf`. Values outside the range will have
      `nan` as output.
  
      ```python
      x = tf.constant([-float("inf"), -1, -0.5, 1, 0, 0.5, 10, float("inf")])
      tf.math.atanh(x) ==> [nan -inf -0.54930615 inf  0. 0.54930615 nan nan]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Atanh", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return atanh_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              atanh, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Atanh", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            atanh, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Atanh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Atanh = tf_export("raw_ops.Atanh")(_ops.to_raw_op(atanh))
  
  
  def atanh_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Atanh", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Atanh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def batch_mat_mul(x, y, adj_x=False, adj_y=False, name=None):
    r"""Multiplies slices of two tensors in batches.
  
    Multiplies all slices of `Tensor` `x` and `y` (each slice can be
    viewed as an element of a batch), and arranges the individual results
    in a single output tensor of the same batch size. Each of the
    individual slices can optionally be adjointed (to adjoint a matrix
    means to transpose and conjugate it) before multiplication by setting
    the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
  
    The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`
    and `[..., r_y, c_y]`.
  
    The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where:
  
        r_o = c_x if adj_x else r_x
        c_o = r_y if adj_y else c_y
  
    It is computed as:
  
        output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
        2-D or higher with shape `[..., r_x, c_x]`.
      y: A `Tensor`. Must have the same type as `x`.
        2-D or higher with shape `[..., r_y, c_y]`.
      adj_x: An optional `bool`. Defaults to `False`.
        If `True`, adjoint the slices of `x`. Defaults to `False`.
      adj_y: An optional `bool`. Defaults to `False`.
        If `True`, adjoint the slices of `y`. Defaults to `False`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "BatchMatMul", name,
          tld.op_callbacks, x, y, "adj_x", adj_x, "adj_y", adj_y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return batch_mat_mul_eager_fallback(
            x, y, adj_x=adj_x, adj_y=adj_y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if adj_x is None:
      adj_x = False
    adj_x = _execute.make_bool(adj_x, "adj_x")
    if adj_y is None:
      adj_y = False
    adj_y = _execute.make_bool(adj_y, "adj_y")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "BatchMatMul", x=x, y=y, adj_x=adj_x, adj_y=adj_y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "adj_x",
                _op._get_attr_bool("adj_x"), "adj_y",
                _op._get_attr_bool("adj_y"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "BatchMatMul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  BatchMatMul = tf_export("raw_ops.BatchMatMul")(_ops.to_raw_op(batch_mat_mul))
  
  
  def batch_mat_mul_eager_fallback(x, y, adj_x, adj_y, name, ctx):
    if adj_x is None:
      adj_x = False
    adj_x = _execute.make_bool(adj_x, "adj_x")
    if adj_y is None:
      adj_y = False
    adj_y = _execute.make_bool(adj_y, "adj_y")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T, "adj_x", adj_x, "adj_y", adj_y)
    _result = _execute.execute(b"BatchMatMul", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "BatchMatMul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def batch_mat_mul_v2(x, y, adj_x=False, adj_y=False, name=None):
    r"""Multiplies slices of two tensors in batches.
  
    Multiplies all slices of `Tensor` `x` and `y` (each slice can be
    viewed as an element of a batch), and arranges the individual results
    in a single output tensor of the same batch size. Each of the
    individual slices can optionally be adjointed (to adjoint a matrix
    means to transpose and conjugate it) before multiplication by setting
    the `adj_x` or `adj_y` flag to `True`, which are by default `False`.
  
    The input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`
    and `[..., r_y, c_y]`.
  
    The output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where:
  
        r_o = c_x if adj_x else r_x
        c_o = r_y if adj_y else c_y
  
    It is computed as:
  
        output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])
  
    *NOTE*: `BatchMatMulV2` supports broadcasting in the batch dimensions. More
    about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
        2-D or higher with shape `[..., r_x, c_x]`.
      y: A `Tensor`. Must have the same type as `x`.
        2-D or higher with shape `[..., r_y, c_y]`.
      adj_x: An optional `bool`. Defaults to `False`.
        If `True`, adjoint the slices of `x`. Defaults to `False`.
      adj_y: An optional `bool`. Defaults to `False`.
        If `True`, adjoint the slices of `y`. Defaults to `False`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "BatchMatMulV2", name,
          tld.op_callbacks, x, y, "adj_x", adj_x, "adj_y", adj_y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return batch_mat_mul_v2_eager_fallback(
            x, y, adj_x=adj_x, adj_y=adj_y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if adj_x is None:
      adj_x = False
    adj_x = _execute.make_bool(adj_x, "adj_x")
    if adj_y is None:
      adj_y = False
    adj_y = _execute.make_bool(adj_y, "adj_y")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "BatchMatMulV2", x=x, y=y, adj_x=adj_x, adj_y=adj_y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "adj_x",
                _op._get_attr_bool("adj_x"), "adj_y",
                _op._get_attr_bool("adj_y"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "BatchMatMulV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  BatchMatMulV2 = tf_export("raw_ops.BatchMatMulV2")(_ops.to_raw_op(batch_mat_mul_v2))
  
  
  def batch_mat_mul_v2_eager_fallback(x, y, adj_x, adj_y, name, ctx):
    if adj_x is None:
      adj_x = False
    adj_x = _execute.make_bool(adj_x, "adj_x")
    if adj_y is None:
      adj_y = False
    adj_y = _execute.make_bool(adj_y, "adj_y")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T, "adj_x", adj_x, "adj_y", adj_y)
    _result = _execute.execute(b"BatchMatMulV2", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "BatchMatMulV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.betainc', v1=['math.betainc', 'betainc'])
  @deprecated_endpoints('betainc')
  def betainc(a, b, x, name=None):
    r"""Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
  
    The regularized incomplete beta integral is defined as:
  
  
    \\(I_x(a, b) = \frac{B(x; a, b)}{B(a, b)}\\)
  
    where
  
  
    \\(B(x; a, b) = \int_0^x t^{a-1} (1 - t)^{b-1} dt\\)
  
  
    is the incomplete beta function and \\(B(a, b)\\) is the *complete*
    beta function.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `float32`, `float64`.
      b: A `Tensor`. Must have the same type as `a`.
      x: A `Tensor`. Must have the same type as `a`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `a`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Betainc", name,
          tld.op_callbacks, a, b, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return betainc_eager_fallback(
            a, b, x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              betainc, (), dict(a=a, b=b, x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Betainc", a=a, b=b, x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            betainc, (), dict(a=a, b=b, x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Betainc", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Betainc = tf_export("raw_ops.Betainc")(_ops.to_raw_op(betainc))
  
  
  def betainc_eager_fallback(a, b, x, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([a, b, x], ctx)
    (a, b, x) = _inputs_T
    _inputs_flat = [a, b, x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Betainc", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Betainc", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def bincount(arr, size, weights, name=None):
    r"""Counts the number of occurrences of each value in an integer array.
  
    Outputs a vector with length `size` and the same dtype as `weights`. If
    `weights` are empty, then index `i` stores the number of times the value `i` is
    counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
    the value in `weights` at each index where the corresponding value in `arr` is
    `i`.
  
    Values in `arr` outside of the range [0, size) are ignored.
  
    Args:
      arr: A `Tensor` of type `int32`. int32 `Tensor`.
      size: A `Tensor` of type `int32`. non-negative int32 scalar `Tensor`.
      weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
        is an int32, int64, float32, or float64 `Tensor` with the same
        shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights
        equal to 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `weights`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Bincount", name,
          tld.op_callbacks, arr, size, weights)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return bincount_eager_fallback(
            arr, size, weights, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Bincount", arr=arr, size=size, weights=weights, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Bincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Bincount = tf_export("raw_ops.Bincount")(_ops.to_raw_op(bincount))
  
  
  def bincount_eager_fallback(arr, size, weights, name, ctx):
    _attr_T, (weights,) = _execute.args_to_matching_eager([weights], ctx)
    arr = _ops.convert_to_tensor(arr, _dtypes.int32)
    size = _ops.convert_to_tensor(size, _dtypes.int32)
    _inputs_flat = [arr, size, weights]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Bincount", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Bincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def bucketize(input, boundaries, name=None):
    r"""Bucketizes 'input' based on 'boundaries'.
  
    For example, if the inputs are
        boundaries = [0, 10, 100]
        input = [[-5, 10000]
                 [150,   10]
                 [5,    100]]
  
    then the output will be
        output = [[0, 3]
                  [3, 2]
                  [1, 3]]
  
    Args:
      input: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
        Any shape of Tensor contains with int or float type.
      boundaries: A list of `floats`.
        A sorted list of floats gives the boundary of the buckets.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `int32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Bucketize", name,
          tld.op_callbacks, input, "boundaries", boundaries)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return bucketize_eager_fallback(
            input, boundaries=boundaries, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if not isinstance(boundaries, (list, tuple)):
      raise TypeError(
          "Expected list for 'boundaries' argument to "
          "'bucketize' Op, not %r." % boundaries)
    boundaries = [_execute.make_float(_f, "boundaries") for _f in boundaries]
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Bucketize", input=input, boundaries=boundaries, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "boundaries",
                _op.get_attr("boundaries"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Bucketize", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Bucketize = tf_export("raw_ops.Bucketize")(_ops.to_raw_op(bucketize))
  
  
  def bucketize_eager_fallback(input, boundaries, name, ctx):
    if not isinstance(boundaries, (list, tuple)):
      raise TypeError(
          "Expected list for 'boundaries' argument to "
          "'bucketize' Op, not %r." % boundaries)
    boundaries = [_execute.make_float(_f, "boundaries") for _f in boundaries]
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _inputs_flat = [input]
    _attrs = ("T", _attr_T, "boundaries", boundaries)
    _result = _execute.execute(b"Bucketize", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Bucketize", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def cast(x, DstT, Truncate=False, name=None):
    r"""Cast x of type SrcT to y of DstT.
  
    Args:
      x: A `Tensor`.
      DstT: A `tf.DType`.
      Truncate: An optional `bool`. Defaults to `False`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `DstT`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Cast", name, tld.op_callbacks,
          x, "DstT", DstT, "Truncate", Truncate)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return cast_eager_fallback(
            x, DstT=DstT, Truncate=Truncate, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    DstT = _execute.make_type(DstT, "DstT")
    if Truncate is None:
      Truncate = False
    Truncate = _execute.make_bool(Truncate, "Truncate")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Cast", x=x, DstT=DstT, Truncate=Truncate, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("SrcT", _op._get_attr_type("SrcT"), "DstT",
                _op._get_attr_type("DstT"), "Truncate",
                _op._get_attr_bool("Truncate"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Cast", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Cast = tf_export("raw_ops.Cast")(_ops.to_raw_op(cast))
  
  
  def cast_eager_fallback(x, DstT, Truncate, name, ctx):
    DstT = _execute.make_type(DstT, "DstT")
    if Truncate is None:
      Truncate = False
    Truncate = _execute.make_bool(Truncate, "Truncate")
    _attr_SrcT, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("SrcT", _attr_SrcT, "DstT", DstT, "Truncate", Truncate)
    _result = _execute.execute(b"Cast", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Cast", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def ceil(x, name=None):
    r"""Returns element-wise smallest integer not less than x.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Ceil", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return ceil_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Ceil", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Ceil", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Ceil = tf_export("raw_ops.Ceil")(_ops.to_raw_op(ceil))
  
  
  def ceil_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Ceil", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Ceil", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _clip_by_value(t, clip_value_min, clip_value_max, name=None):
    r"""Clips tensor values to a specified min and max.
  
    Given a tensor `t`, this operation returns a tensor of the same type and
    shape as `t` with its values clipped to `clip_value_min` and `clip_value_max`.
    Any values less than `clip_value_min` are set to `clip_value_min`. Any values
    greater than `clip_value_max` are set to `clip_value_max`.
  
    Args:
      t: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        A `Tensor`.
      clip_value_min: A `Tensor`. Must have the same type as `t`.
        A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
        as `t`. The minimum value to clip by.
      clip_value_max: A `Tensor`. Must have the same type as `t`.
        A 0-D (scalar) `Tensor`, or a `Tensor` with the same shape
        as `t`. The maximum value to clip by.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `t`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "ClipByValue", name,
          tld.op_callbacks, t, clip_value_min, clip_value_max)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _clip_by_value_eager_fallback(
            t, clip_value_min, clip_value_max, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "ClipByValue", t=t, clip_value_min=clip_value_min,
                         clip_value_max=clip_value_max, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "ClipByValue", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  ClipByValue = tf_export("raw_ops.ClipByValue")(_ops.to_raw_op(_clip_by_value))
  
  
  def _clip_by_value_eager_fallback(t, clip_value_min, clip_value_max, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([t, clip_value_min, clip_value_max], ctx)
    (t, clip_value_min, clip_value_max) = _inputs_T
    _inputs_flat = [t, clip_value_min, clip_value_max]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"ClipByValue", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "ClipByValue", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def compare_and_bitpack(input, threshold, name=None):
    r"""Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
  
    Each comparison returns a boolean `true` (if `input_value > threshold`)
    or and `false` otherwise.
  
    This operation is useful for Locality-Sensitive-Hashing (LSH) and other
    algorithms that use hashing approximations of cosine and `L2` distances;
    codes can be generated from an input via:
  
    ```python
    codebook_size = 50
    codebook_bits = codebook_size * 32
    codebook = tf.get_variable('codebook', [x.shape[-1].value, codebook_bits],
                               dtype=x.dtype,
                               initializer=tf.orthogonal_initializer())
    codes = compare_and_threshold(tf.matmul(x, codebook), threshold=0.)
    codes = tf.bitcast(codes, tf.int32)  # go from uint8 to int32
    # now codes has shape x.shape[:-1] + [codebook_size]
    ```
  
    **NOTE**: Currently, the innermost dimension of the tensor must be divisible
    by 8.
  
    Given an `input` shaped `[s0, s1, ..., s_n]`, the output is
    a `uint8` tensor shaped `[s0, s1, ..., s_n / 8]`.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `bool`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`.
        Values to compare against `threshold` and bitpack.
      threshold: A `Tensor`. Must have the same type as `input`.
        Threshold to compare against.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `uint8`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "CompareAndBitpack", name,
          tld.op_callbacks, input, threshold)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return compare_and_bitpack_eager_fallback(
            input, threshold, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "CompareAndBitpack", input=input, threshold=threshold, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "CompareAndBitpack", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  CompareAndBitpack = tf_export("raw_ops.CompareAndBitpack")(_ops.to_raw_op(compare_and_bitpack))
  
  
  def compare_and_bitpack_eager_fallback(input, threshold, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([input, threshold], ctx)
    (input, threshold) = _inputs_T
    _inputs_flat = [input, threshold]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"CompareAndBitpack", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "CompareAndBitpack", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _complex(real, imag, Tout=_dtypes.complex64, name=None):
    r"""Converts two real numbers to a complex number.
  
    Given a tensor `real` representing the real part of a complex number, and a
    tensor `imag` representing the imaginary part of a complex number, this
    operation returns complex numbers elementwise of the form \\(a + bj\\), where
    *a* represents the `real` part and *b* represents the `imag` part.
  
    The input tensors `real` and `imag` must have the same shape.
  
    For example:
  
    ```
    # tensor 'real' is [2.25, 3.25]
    # tensor `imag` is [4.75, 5.75]
    tf.complex(real, imag) ==> [[2.25 + 4.75j], [3.25 + 5.75j]]
    ```
  
    Args:
      real: A `Tensor`. Must be one of the following types: `float32`, `float64`.
      imag: A `Tensor`. Must have the same type as `real`.
      Tout: An optional `tf.DType` from: `tf.complex64, tf.complex128`. Defaults to `tf.complex64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `Tout`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Complex", name,
          tld.op_callbacks, real, imag, "Tout", Tout)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _complex_eager_fallback(
            real, imag, Tout=Tout, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Tout is None:
      Tout = _dtypes.complex64
    Tout = _execute.make_type(Tout, "Tout")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Complex", real=real, imag=imag, Tout=Tout, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tout",
                _op._get_attr_type("Tout"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Complex", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Complex = tf_export("raw_ops.Complex")(_ops.to_raw_op(_complex))
  
  
  def _complex_eager_fallback(real, imag, Tout, name, ctx):
    if Tout is None:
      Tout = _dtypes.complex64
    Tout = _execute.make_type(Tout, "Tout")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([real, imag], ctx, _dtypes.float32)
    (real, imag) = _inputs_T
    _inputs_flat = [real, imag]
    _attrs = ("T", _attr_T, "Tout", Tout)
    _result = _execute.execute(b"Complex", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Complex", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def complex_abs(x, Tout=_dtypes.float32, name=None):
    r"""Computes the complex absolute value of a tensor.
  
    Given a tensor `x` of complex numbers, this operation returns a tensor of type
    `float` or `double` that is the absolute value of each element in `x`. All
    elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
    value is computed as \\( \sqrt{a^2 + b^2}\\).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
      Tout: An optional `tf.DType` from: `tf.float32, tf.float64`. Defaults to `tf.float32`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `Tout`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "ComplexAbs", name,
          tld.op_callbacks, x, "Tout", Tout)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return complex_abs_eager_fallback(
            x, Tout=Tout, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "ComplexAbs", x=x, Tout=Tout, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tout",
                _op._get_attr_type("Tout"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "ComplexAbs", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  ComplexAbs = tf_export("raw_ops.ComplexAbs")(_ops.to_raw_op(complex_abs))
  
  
  def complex_abs_eager_fallback(x, Tout, name, ctx):
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx, _dtypes.complex64)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T, "Tout", Tout)
    _result = _execute.execute(b"ComplexAbs", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "ComplexAbs", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def conj(input, name=None):
    r"""Returns the complex conjugate of a complex number.
  
    Given a tensor `input` of complex numbers, this operation returns a tensor of
    complex numbers that are the complex conjugate of each element in `input`. The
    complex numbers in `input` must be of the form \\(a + bj\\), where *a* is the
    real part and *b* is the imaginary part.
  
    The complex conjugate returned by this operation is of the form \\(a - bj\\).
  
    For example:
  
    ```
    # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
    tf.conj(input) ==> [-2.25 - 4.75j, 3.25 - 5.75j]
    ```
  
    Args:
      input: A `Tensor`. Must be one of the following types: `complex64`, `complex128`, `variant`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Conj", name, tld.op_callbacks,
          input)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return conj_eager_fallback(
            input, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Conj", input=input, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Conj", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Conj = tf_export("raw_ops.Conj")(_ops.to_raw_op(conj))
  
  
  def conj_eager_fallback(input, name, ctx):
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, _dtypes.complex64)
    _inputs_flat = [input]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Conj", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Conj", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.cos', 'cos')
  def cos(x, name=None):
    r"""Computes cos of x element-wise.
  
      Given an input tensor, this function computes cosine of every
      element in the tensor. Input range is `(-inf, inf)` and
      output range is `[-1,1]`. If input lies outside the boundary, `nan`
      is returned.
  
      ```python
      x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])
      tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Cos", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return cos_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              cos, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Cos", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            cos, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Cos", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Cos = tf_export("raw_ops.Cos")(_ops.to_raw_op(cos))
  
  
  def cos_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Cos", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Cos", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.cosh', 'cosh')
  def cosh(x, name=None):
    r"""Computes hyperbolic cosine of x element-wise.
  
      Given an input tensor, this function computes hyperbolic cosine of every
      element in the tensor. Input range is `[-inf, inf]` and output range
      is `[1, inf]`.
  
      ```python
      x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
      tf.math.cosh(x) ==> [inf 4.0515420e+03 1.1276259e+00 1.5430807e+00 1.8106556e+00 3.7621956e+00 1.1013233e+04 inf]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Cosh", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return cosh_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              cosh, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Cosh", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            cosh, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Cosh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Cosh = tf_export("raw_ops.Cosh")(_ops.to_raw_op(cosh))
  
  
  def cosh_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Cosh", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Cosh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('linalg.cross', v1=['linalg.cross', 'cross'])
  @deprecated_endpoints('cross')
  def cross(a, b, name=None):
    r"""Compute the pairwise cross product.
  
    `a` and `b` must be the same shape; they can either be simple 3-element vectors,
    or any shape where the innermost dimension is 3. In the latter case, each pair
    of corresponding 3-element vectors is cross-multiplied independently.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
        A tensor containing 3-element vectors.
      b: A `Tensor`. Must have the same type as `a`.
        Another tensor, of same type and shape as `a`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `a`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Cross", name,
          tld.op_callbacks, a, b)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return cross_eager_fallback(
            a, b, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              cross, (), dict(a=a, b=b, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Cross", a=a, b=b, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            cross, (), dict(a=a, b=b, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Cross", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Cross = tf_export("raw_ops.Cross")(_ops.to_raw_op(cross))
  
  
  def cross_eager_fallback(a, b, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([a, b], ctx)
    (a, b) = _inputs_T
    _inputs_flat = [a, b]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Cross", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Cross", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def cumprod(x, axis, exclusive=False, reverse=False, name=None):
    r"""Compute the cumulative product of the tensor `x` along `axis`.
  
    By default, this op performs an inclusive cumprod, which means that the first
    element of the input is identical to the first element of the output:
  
    ```python
    tf.cumprod([a, b, c])  # => [a, a * b, a * b * c]
    ```
  
    By setting the `exclusive` kwarg to `True`, an exclusive cumprod is
    performed instead:
  
    ```python
    tf.cumprod([a, b, c], exclusive=True)  # => [1, a, a * b]
    ```
  
    By setting the `reverse` kwarg to `True`, the cumprod is performed in the
    opposite direction:
  
    ```python
    tf.cumprod([a, b, c], reverse=True)  # => [a * b * c, b * c, c]
    ```
  
    This is more efficient than using separate `tf.reverse` ops.
  
    The `reverse` and `exclusive` kwargs can also be combined:
  
    ```python
    tf.cumprod([a, b, c], exclusive=True, reverse=True)  # => [b * c, c, 1]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        A `Tensor`. Must be one of the following types: `float32`, `float64`,
        `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
        `complex128`, `qint8`, `quint8`, `qint32`, `half`.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A `Tensor` of type `int32` (default: 0). Must be in the range
        `[-rank(x), rank(x))`.
      exclusive: An optional `bool`. Defaults to `False`.
        If `True`, perform exclusive cumprod.
      reverse: An optional `bool`. Defaults to `False`.
        A `bool` (default: False).
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Cumprod", name,
          tld.op_callbacks, x, axis, "exclusive", exclusive, "reverse", reverse)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return cumprod_eager_fallback(
            x, axis, exclusive=exclusive, reverse=reverse, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if exclusive is None:
      exclusive = False
    exclusive = _execute.make_bool(exclusive, "exclusive")
    if reverse is None:
      reverse = False
    reverse = _execute.make_bool(reverse, "reverse")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Cumprod", x=x, axis=axis, exclusive=exclusive, reverse=reverse,
                     name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("exclusive", _op._get_attr_bool("exclusive"), "reverse",
                _op._get_attr_bool("reverse"), "T", _op._get_attr_type("T"),
                "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Cumprod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Cumprod = tf_export("raw_ops.Cumprod")(_ops.to_raw_op(cumprod))
  
  
  def cumprod_eager_fallback(x, axis, exclusive, reverse, name, ctx):
    if exclusive is None:
      exclusive = False
    exclusive = _execute.make_bool(exclusive, "exclusive")
    if reverse is None:
      reverse = False
    reverse = _execute.make_bool(reverse, "reverse")
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [x, axis]
    _attrs = ("exclusive", exclusive, "reverse", reverse, "T", _attr_T, "Tidx",
    _attr_Tidx)
    _result = _execute.execute(b"Cumprod", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Cumprod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def cumsum(x, axis, exclusive=False, reverse=False, name=None):
    r"""Compute the cumulative sum of the tensor `x` along `axis`.
  
    By default, this op performs an inclusive cumsum, which means that the first
    element of the input is identical to the first element of the output:
  
    ```python
    tf.cumsum([a, b, c])  # => [a, a + b, a + b + c]
    ```
  
    By setting the `exclusive` kwarg to `True`, an exclusive cumsum is
    performed instead:
  
    ```python
    tf.cumsum([a, b, c], exclusive=True)  # => [0, a, a + b]
    ```
  
    By setting the `reverse` kwarg to `True`, the cumsum is performed in the
    opposite direction:
  
    ```python
    tf.cumsum([a, b, c], reverse=True)  # => [a + b + c, b + c, c]
    ```
  
    This is more efficient than using separate `tf.reverse` ops.
  
    The `reverse` and `exclusive` kwargs can also be combined:
  
    ```python
    tf.cumsum([a, b, c], exclusive=True, reverse=True)  # => [b + c, c, 0]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        A `Tensor`. Must be one of the following types: `float32`, `float64`,
        `int64`, `int32`, `uint8`, `uint16`, `int16`, `int8`, `complex64`,
        `complex128`, `qint8`, `quint8`, `qint32`, `half`.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A `Tensor` of type `int32` (default: 0). Must be in the range
        `[-rank(x), rank(x))`.
      exclusive: An optional `bool`. Defaults to `False`.
        If `True`, perform exclusive cumsum.
      reverse: An optional `bool`. Defaults to `False`.
        A `bool` (default: False).
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Cumsum", name,
          tld.op_callbacks, x, axis, "exclusive", exclusive, "reverse", reverse)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return cumsum_eager_fallback(
            x, axis, exclusive=exclusive, reverse=reverse, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if exclusive is None:
      exclusive = False
    exclusive = _execute.make_bool(exclusive, "exclusive")
    if reverse is None:
      reverse = False
    reverse = _execute.make_bool(reverse, "reverse")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Cumsum", x=x, axis=axis, exclusive=exclusive, reverse=reverse,
                    name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("exclusive", _op._get_attr_bool("exclusive"), "reverse",
                _op._get_attr_bool("reverse"), "T", _op._get_attr_type("T"),
                "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Cumsum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Cumsum = tf_export("raw_ops.Cumsum")(_ops.to_raw_op(cumsum))
  
  
  def cumsum_eager_fallback(x, axis, exclusive, reverse, name, ctx):
    if exclusive is None:
      exclusive = False
    exclusive = _execute.make_bool(exclusive, "exclusive")
    if reverse is None:
      reverse = False
    reverse = _execute.make_bool(reverse, "reverse")
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [x, axis]
    _attrs = ("exclusive", exclusive, "reverse", reverse, "T", _attr_T, "Tidx",
    _attr_Tidx)
    _result = _execute.execute(b"Cumsum", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Cumsum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def cumulative_logsumexp(x, axis, exclusive=False, reverse=False, name=None):
    r"""Compute the cumulative product of the tensor `x` along `axis`.
  
    By default, this op performs an inclusive cumulative log-sum-exp,
    which means that the first
    element of the input is identical to the first element of the output:
    ```python
    tf.math.cumulative_logsumexp([a, b, c])  # => [a, log(exp(a) + exp(b)), log(exp(a) + exp(b) + exp(c))]
    ```
  
    By setting the `exclusive` kwarg to `True`, an exclusive cumulative log-sum-exp is
    performed instead:
    ```python
    tf.cumulative_logsumexp([a, b, c], exclusive=True)  # => [-inf, a, log(exp(a) * exp(b))]
    ```
    Note that the neutral element of the log-sum-exp operation is `-inf`,
    however, for performance reasons, the minimal value representable by the
    floating point type is used instead.
  
    By setting the `reverse` kwarg to `True`, the cumulative log-sum-exp is performed in the
    opposite direction.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`.
        A `Tensor`. Must be one of the following types: `float16`, `float32`, `float64`.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A `Tensor` of type `int32` (default: 0). Must be in the range
        `[-rank(x), rank(x))`.
      exclusive: An optional `bool`. Defaults to `False`.
        If `True`, perform exclusive cumulative log-sum-exp.
      reverse: An optional `bool`. Defaults to `False`.
        A `bool` (default: False).
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "CumulativeLogsumexp", name,
          tld.op_callbacks, x, axis, "exclusive", exclusive, "reverse", reverse)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return cumulative_logsumexp_eager_fallback(
            x, axis, exclusive=exclusive, reverse=reverse, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if exclusive is None:
      exclusive = False
    exclusive = _execute.make_bool(exclusive, "exclusive")
    if reverse is None:
      reverse = False
    reverse = _execute.make_bool(reverse, "reverse")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "CumulativeLogsumexp", x=x, axis=axis, exclusive=exclusive,
                                 reverse=reverse, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("exclusive", _op._get_attr_bool("exclusive"), "reverse",
                _op._get_attr_bool("reverse"), "T", _op._get_attr_type("T"),
                "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "CumulativeLogsumexp", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  CumulativeLogsumexp = tf_export("raw_ops.CumulativeLogsumexp")(_ops.to_raw_op(cumulative_logsumexp))
  
  
  def cumulative_logsumexp_eager_fallback(x, axis, exclusive, reverse, name, ctx):
    if exclusive is None:
      exclusive = False
    exclusive = _execute.make_bool(exclusive, "exclusive")
    if reverse is None:
      reverse = False
    reverse = _execute.make_bool(reverse, "reverse")
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [x, axis]
    _attrs = ("exclusive", exclusive, "reverse", reverse, "T", _attr_T, "Tidx",
    _attr_Tidx)
    _result = _execute.execute(b"CumulativeLogsumexp", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "CumulativeLogsumexp", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def dense_bincount(input, size, weights, binary_output=False, name=None):
    r"""Counts the number of occurrences of each value in an integer array.
  
    Outputs a vector with length `size` and the same dtype as `weights`. If
    `weights` are empty, then index `i` stores the number of times the value `i` is
    counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
    the value in `weights` at each index where the corresponding value in `arr` is
    `i`.
  
    Values in `arr` outside of the range [0, size) are ignored.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        1D or 2D int `Tensor`.
      size: A `Tensor`. Must have the same type as `input`.
        non-negative int scalar `Tensor`.
      weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
        is an int32, int64, float32, or float64 `Tensor` with the same
        shape as `arr`, or a length-0 `Tensor`, in which case it acts as all weights
        equal to 1.
      binary_output: An optional `bool`. Defaults to `False`.
        bool; Whether the kernel should count the appearance or number of occurrences.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `weights`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "DenseBincount", name,
          tld.op_callbacks, input, size, weights, "binary_output",
          binary_output)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return dense_bincount_eager_fallback(
            input, size, weights, binary_output=binary_output, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if binary_output is None:
      binary_output = False
    binary_output = _execute.make_bool(binary_output, "binary_output")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "DenseBincount", input=input, size=size, weights=weights,
                           binary_output=binary_output, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("Tidx", _op._get_attr_type("Tidx"), "T",
                _op._get_attr_type("T"), "binary_output",
                _op._get_attr_bool("binary_output"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "DenseBincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  DenseBincount = tf_export("raw_ops.DenseBincount")(_ops.to_raw_op(dense_bincount))
  
  
  def dense_bincount_eager_fallback(input, size, weights, binary_output, name, ctx):
    if binary_output is None:
      binary_output = False
    binary_output = _execute.make_bool(binary_output, "binary_output")
    _attr_Tidx, _inputs_Tidx = _execute.args_to_matching_eager([input, size], ctx)
    (input, size) = _inputs_Tidx
    _attr_T, (weights,) = _execute.args_to_matching_eager([weights], ctx)
    _inputs_flat = [input, size, weights]
    _attrs = ("Tidx", _attr_Tidx, "T", _attr_T, "binary_output", binary_output)
    _result = _execute.execute(b"DenseBincount", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "DenseBincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.digamma', v1=['math.digamma', 'digamma'])
  @deprecated_endpoints('digamma')
  def digamma(x, name=None):
    r"""Computes Psi, the derivative of Lgamma (the log of the absolute value of
  
    `Gamma(x)`), element-wise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Digamma", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return digamma_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              digamma, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Digamma", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            digamma, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Digamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Digamma = tf_export("raw_ops.Digamma")(_ops.to_raw_op(digamma))
  
  
  def digamma_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Digamma", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Digamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def div(x, y, name=None):
    r"""Returns x / y element-wise.
  
    *NOTE*: `Div` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Div", name, tld.op_callbacks,
          x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return div_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Div", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Div", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Div = tf_export("raw_ops.Div")(_ops.to_raw_op(div))
  
  
  def div_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Div", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Div", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def div_no_nan(x, y, name=None):
    r"""Returns 0 if the denominator is zero.
  
    
    *NOTE*: `DivNoNan` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "DivNoNan", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return div_no_nan_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "DivNoNan", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "DivNoNan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  DivNoNan = tf_export("raw_ops.DivNoNan")(_ops.to_raw_op(div_no_nan))
  
  
  def div_no_nan_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"DivNoNan", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "DivNoNan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def equal(x, y, incompatible_shape_error=True, name=None):
    r"""Returns the truth value of (x == y) element-wise.
  
    *NOTE*: `Equal` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    ```python
    x = tf.constant([2, 4])
    y = tf.constant(2)
    tf.math.equal(x, y) ==> array([True, False])
  
    x = tf.constant([2, 4])
    y = tf.constant([2, 4])
    tf.math.equal(x, y) ==> array([True,  True])
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `uint16`, `uint32`, `uint64`, `complex64`, `quint8`, `qint8`, `qint32`, `string`, `bool`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      incompatible_shape_error: An optional `bool`. Defaults to `True`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Equal", name,
          tld.op_callbacks, x, y, "incompatible_shape_error",
          incompatible_shape_error)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return equal_eager_fallback(
            x, y, incompatible_shape_error=incompatible_shape_error, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if incompatible_shape_error is None:
      incompatible_shape_error = True
    incompatible_shape_error = _execute.make_bool(incompatible_shape_error, "incompatible_shape_error")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Equal", x=x, y=y, incompatible_shape_error=incompatible_shape_error,
                   name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "incompatible_shape_error",
                _op._get_attr_bool("incompatible_shape_error"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Equal", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Equal = tf_export("raw_ops.Equal")(_ops.to_raw_op(equal))
  
  
  def equal_eager_fallback(x, y, incompatible_shape_error, name, ctx):
    if incompatible_shape_error is None:
      incompatible_shape_error = True
    incompatible_shape_error = _execute.make_bool(incompatible_shape_error, "incompatible_shape_error")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T, "incompatible_shape_error",
    incompatible_shape_error)
    _result = _execute.execute(b"Equal", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Equal", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.erf', v1=['math.erf', 'erf'])
  @deprecated_endpoints('erf')
  def erf(x, name=None):
    r"""Computes the Gauss error function of `x` element-wise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Erf", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return erf_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              erf, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Erf", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            erf, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Erf", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Erf = tf_export("raw_ops.Erf")(_ops.to_raw_op(erf))
  
  
  def erf_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Erf", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Erf", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.erfc', v1=['math.erfc', 'erfc'])
  @deprecated_endpoints('erfc')
  def erfc(x, name=None):
    r"""Computes the complementary error function of `x` element-wise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Erfc", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return erfc_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              erfc, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Erfc", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            erfc, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Erfc", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Erfc = tf_export("raw_ops.Erfc")(_ops.to_raw_op(erfc))
  
  
  def erfc_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Erfc", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Erfc", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def erfinv(x, name=None):
    r"""TODO: add doc.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Erfinv", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return erfinv_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Erfinv", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Erfinv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Erfinv = tf_export("raw_ops.Erfinv")(_ops.to_raw_op(erfinv))
  
  
  def erfinv_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Erfinv", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Erfinv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def euclidean_norm(input, axis, keep_dims=False, name=None):
    r"""Computes the euclidean norm of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "EuclideanNorm", name,
          tld.op_callbacks, input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return euclidean_norm_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "EuclideanNorm", input=input, reduction_indices=axis,
                           keep_dims=keep_dims, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
                _op._get_attr_type("T"), "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "EuclideanNorm", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  EuclideanNorm = tf_export("raw_ops.EuclideanNorm")(_ops.to_raw_op(euclidean_norm))
  
  
  def euclidean_norm_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"EuclideanNorm", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "EuclideanNorm", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def exp(x, name=None):
    r"""Computes exponential of x element-wise.  \\(y = e^x\\).
  
      This function computes the exponential of every element in the input tensor.
      i.e. `exp(x)` or `e^(x)`, where `x` is the input tensor.
      `e` denotes Euler's number and is approximately equal to 2.718281.
      Output is positive for any real input.
  
      ```python
      x = tf.constant(2.0)
      tf.math.exp(x) ==> 7.389056
  
      x = tf.constant([2.0, 8.0])
      tf.math.exp(x) ==> array([7.389056, 2980.958], dtype=float32)
      ```
  
      For complex numbers, the exponential value is calculated as follows:
  
      ```
      e^(x+iy) = e^x * e^iy = e^x * (cos y + i sin y)
      ```
  
      Let's consider complex number 1+1j as an example.
      e^1 * (cos 1 + i sin 1) = 2.7182818284590 * (0.54030230586+0.8414709848j)
  
      ```python
      x = tf.constant(1 + 1j)
      tf.math.exp(x) ==> 1.4686939399158851+2.2873552871788423j
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Exp", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return exp_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Exp", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Exp", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Exp = tf_export("raw_ops.Exp")(_ops.to_raw_op(exp))
  
  
  def exp_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Exp", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Exp", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.expm1', v1=['math.expm1', 'expm1'])
  @deprecated_endpoints('expm1')
  def expm1(x, name=None):
    r"""Computes `exp(x) - 1` element-wise.
  
      i.e. `exp(x) - 1` or `e^(x) - 1`, where `x` is the input tensor.
      `e` denotes Euler's number and is approximately equal to 2.718281.
  
      ```python
      x = tf.constant(2.0)
      tf.math.expm1(x) ==> 6.389056
  
      x = tf.constant([2.0, 8.0])
      tf.math.expm1(x) ==> array([6.389056, 2979.958], dtype=float32)
  
      x = tf.constant(1 + 1j)
      tf.math.expm1(x) ==> (0.46869393991588515+2.2873552871788423j)
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Expm1", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return expm1_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              expm1, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Expm1", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            expm1, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Expm1", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Expm1 = tf_export("raw_ops.Expm1")(_ops.to_raw_op(expm1))
  
  
  def expm1_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Expm1", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Expm1", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.floor', 'floor')
  def floor(x, name=None):
    r"""Returns element-wise largest integer not greater than x.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Floor", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return floor_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              floor, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Floor", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            floor, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Floor", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Floor = tf_export("raw_ops.Floor")(_ops.to_raw_op(floor))
  
  
  def floor_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Floor", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Floor", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export(v1=['floor_div'])
  @deprecated_endpoints('floor_div')
  def floor_div(x, y, name=None):
    r"""Returns x // y element-wise.
  
    *NOTE*: `floor_div` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "FloorDiv", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return floor_div_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              floor_div, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "FloorDiv", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            floor_div, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "FloorDiv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  FloorDiv = tf_export("raw_ops.FloorDiv")(_ops.to_raw_op(floor_div))
  
  
  def floor_div_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"FloorDiv", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "FloorDiv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.floormod', 'math.mod', v1=['math.floormod', 'floormod', 'math.mod', 'mod'])
  @deprecated_endpoints('floormod', 'mod')
  def floor_mod(x, y, name=None):
    r"""Returns element-wise remainder of division. When `x < 0` xor `y < 0` is
  
    true, this follows Python semantics in that the result here is consistent
    with a flooring divide. E.g. `floor(x / y) * y + mod(x, y) = x`.
  
    *NOTE*: `math.floormod` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `int32`, `int64`, `uint64`, `bfloat16`, `half`, `float32`, `float64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "FloorMod", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return floor_mod_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              floor_mod, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "FloorMod", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            floor_mod, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "FloorMod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  FloorMod = tf_export("raw_ops.FloorMod")(_ops.to_raw_op(floor_mod))
  
  
  def floor_mod_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"FloorMod", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "FloorMod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.greater', 'greater')
  def greater(x, y, name=None):
    r"""Returns the truth value of (x > y) element-wise.
  
    *NOTE*: `math.greater` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Example:
  
    ```python
    x = tf.constant([5, 4, 6])
    y = tf.constant([5, 2, 5])
    tf.math.greater(x, y) ==> [False, True, True]
  
    x = tf.constant([5, 4, 6])
    y = tf.constant([5])
    tf.math.greater(x, y) ==> [False, False, True]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Greater", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return greater_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              greater, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Greater", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            greater, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Greater", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Greater = tf_export("raw_ops.Greater")(_ops.to_raw_op(greater))
  
  
  def greater_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Greater", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Greater", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.greater_equal', 'greater_equal')
  def greater_equal(x, y, name=None):
    r"""Returns the truth value of (x >= y) element-wise.
  
    *NOTE*: `math.greater_equal` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Example:
  
    ```python
    x = tf.constant([5, 4, 6, 7])
    y = tf.constant([5, 2, 5, 10])
    tf.math.greater_equal(x, y) ==> [True, True, True, False]
  
    x = tf.constant([5, 4, 6, 7])
    y = tf.constant([5])
    tf.math.greater_equal(x, y) ==> [True, False, True, True]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "GreaterEqual", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return greater_equal_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              greater_equal, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "GreaterEqual", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            greater_equal, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "GreaterEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  GreaterEqual = tf_export("raw_ops.GreaterEqual")(_ops.to_raw_op(greater_equal))
  
  
  def greater_equal_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"GreaterEqual", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "GreaterEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _histogram_fixed_width(values, value_range, nbins, dtype=_dtypes.int32, name=None):
    r"""Return histogram of values.
  
    Given the tensor `values`, this operation returns a rank 1 histogram counting
    the number of entries in `values` that fall into every bin.  The bins are
    equal width and determined by the arguments `value_range` and `nbins`.
  
    ```python
    # Bins will be:  (-inf, 1), [1, 2), [2, 3), [3, 4), [4, inf)
    nbins = 5
    value_range = [0.0, 5.0]
    new_values = [-1.0, 0.0, 1.5, 2.0, 5.0, 15]
  
    with tf.get_default_session() as sess:
      hist = tf.histogram_fixed_width(new_values, value_range, nbins=5)
      variables.global_variables_initializer().run()
      sess.run(hist) => [2, 1, 1, 0, 2]
    ```
  
    Args:
      values: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
        Numeric `Tensor`.
      value_range: A `Tensor`. Must have the same type as `values`.
        Shape [2] `Tensor` of same `dtype` as `values`.
        values <= value_range[0] will be mapped to hist[0],
        values >= value_range[1] will be mapped to hist[-1].
      nbins: A `Tensor` of type `int32`.
        Scalar `int32 Tensor`.  Number of histogram bins.
      dtype: An optional `tf.DType` from: `tf.int32, tf.int64`. Defaults to `tf.int32`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `dtype`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "HistogramFixedWidth", name,
          tld.op_callbacks, values, value_range, nbins, "dtype", dtype)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _histogram_fixed_width_eager_fallback(
            values, value_range, nbins, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if dtype is None:
      dtype = _dtypes.int32
    dtype = _execute.make_type(dtype, "dtype")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "HistogramFixedWidth", values=values, value_range=value_range,
                                 nbins=nbins, dtype=dtype, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "dtype",
                _op._get_attr_type("dtype"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "HistogramFixedWidth", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  HistogramFixedWidth = tf_export("raw_ops.HistogramFixedWidth")(_ops.to_raw_op(_histogram_fixed_width))
  
  
  def _histogram_fixed_width_eager_fallback(values, value_range, nbins, dtype, name, ctx):
    if dtype is None:
      dtype = _dtypes.int32
    dtype = _execute.make_type(dtype, "dtype")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([values, value_range], ctx)
    (values, value_range) = _inputs_T
    nbins = _ops.convert_to_tensor(nbins, _dtypes.int32)
    _inputs_flat = [values, value_range, nbins]
    _attrs = ("T", _attr_T, "dtype", dtype)
    _result = _execute.execute(b"HistogramFixedWidth", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "HistogramFixedWidth", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.igamma', v1=['math.igamma', 'igamma'])
  @deprecated_endpoints('igamma')
  def igamma(a, x, name=None):
    r"""Compute the lower regularized incomplete Gamma function `P(a, x)`.
  
    The lower regularized incomplete Gamma function is defined as:
  
  
    \\(P(a, x) = gamma(a, x) / Gamma(a) = 1 - Q(a, x)\\)
  
    where
  
    \\(gamma(a, x) = \\int_{0}^{x} t^{a-1} exp(-t) dt\\)
  
    is the lower incomplete Gamma function.
  
    Note, above `Q(a, x)` (`Igammac`) is the upper regularized complete
    Gamma function.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `float32`, `float64`.
      x: A `Tensor`. Must have the same type as `a`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `a`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Igamma", name,
          tld.op_callbacks, a, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return igamma_eager_fallback(
            a, x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              igamma, (), dict(a=a, x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Igamma", a=a, x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            igamma, (), dict(a=a, x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Igamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Igamma = tf_export("raw_ops.Igamma")(_ops.to_raw_op(igamma))
  
  
  def igamma_eager_fallback(a, x, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([a, x], ctx)
    (a, x) = _inputs_T
    _inputs_flat = [a, x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Igamma", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Igamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def igamma_grad_a(a, x, name=None):
    r"""Computes the gradient of `igamma(a, x)` wrt `a`.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `float32`, `float64`.
      x: A `Tensor`. Must have the same type as `a`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `a`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "IgammaGradA", name,
          tld.op_callbacks, a, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return igamma_grad_a_eager_fallback(
            a, x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "IgammaGradA", a=a, x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "IgammaGradA", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  IgammaGradA = tf_export("raw_ops.IgammaGradA")(_ops.to_raw_op(igamma_grad_a))
  
  
  def igamma_grad_a_eager_fallback(a, x, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([a, x], ctx)
    (a, x) = _inputs_T
    _inputs_flat = [a, x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"IgammaGradA", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "IgammaGradA", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.igammac', v1=['math.igammac', 'igammac'])
  @deprecated_endpoints('igammac')
  def igammac(a, x, name=None):
    r"""Compute the upper regularized incomplete Gamma function `Q(a, x)`.
  
    The upper regularized incomplete Gamma function is defined as:
  
    \\(Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)\\)
  
    where
  
    \\(Gamma(a, x) = int_{x}^{\infty} t^{a-1} exp(-t) dt\\)
  
    is the upper incomplete Gama function.
  
    Note, above `P(a, x)` (`Igamma`) is the lower regularized complete
    Gamma function.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `float32`, `float64`.
      x: A `Tensor`. Must have the same type as `a`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `a`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Igammac", name,
          tld.op_callbacks, a, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return igammac_eager_fallback(
            a, x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              igammac, (), dict(a=a, x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Igammac", a=a, x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            igammac, (), dict(a=a, x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Igammac", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Igammac = tf_export("raw_ops.Igammac")(_ops.to_raw_op(igammac))
  
  
  def igammac_eager_fallback(a, x, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([a, x], ctx)
    (a, x) = _inputs_T
    _inputs_flat = [a, x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Igammac", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Igammac", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def imag(input, Tout=_dtypes.float32, name=None):
    r"""Returns the imaginary part of a complex number.
  
    Given a tensor `input` of complex numbers, this operation returns a tensor of
    type `float` that is the imaginary part of each element in `input`. All
    elements in `input` must be complex numbers of the form \\(a + bj\\), where *a*
    is the real part and *b* is the imaginary part returned by this operation.
  
    For example:
  
    ```
    # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
    tf.imag(input) ==> [4.75, 5.75]
    ```
  
    Args:
      input: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
      Tout: An optional `tf.DType` from: `tf.float32, tf.float64`. Defaults to `tf.float32`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `Tout`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Imag", name, tld.op_callbacks,
          input, "Tout", Tout)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return imag_eager_fallback(
            input, Tout=Tout, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Imag", input=input, Tout=Tout, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tout",
                _op._get_attr_type("Tout"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Imag", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Imag = tf_export("raw_ops.Imag")(_ops.to_raw_op(imag))
  
  
  def imag_eager_fallback(input, Tout, name, ctx):
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, _dtypes.complex64)
    _inputs_flat = [input]
    _attrs = ("T", _attr_T, "Tout", Tout)
    _result = _execute.execute(b"Imag", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Imag", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def inv(x, name=None):
    r"""Computes the reciprocal of x element-wise.
  
    I.e., \\(y = 1 / x\\).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Inv", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return inv_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Inv", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Inv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Inv = tf_export("raw_ops.Inv")(_ops.to_raw_op(inv))
  
  
  def inv_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Inv", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Inv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def inv_grad(y, dy, name=None):
    r"""Computes the gradient for the inverse of `x` wrt its input.
  
    Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
    is the corresponding input gradient.
  
    Args:
      y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      dy: A `Tensor`. Must have the same type as `y`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `y`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "InvGrad", name,
          tld.op_callbacks, y, dy)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return inv_grad_eager_fallback(
            y, dy, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "InvGrad", y=y, dy=dy, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "InvGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  InvGrad = tf_export("raw_ops.InvGrad")(_ops.to_raw_op(inv_grad))
  
  
  def inv_grad_eager_fallback(y, dy, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([y, dy], ctx)
    (y, dy) = _inputs_T
    _inputs_flat = [y, dy]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"InvGrad", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "InvGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.is_finite', v1=['math.is_finite', 'debugging.is_finite', 'is_finite'])
  @deprecated_endpoints('debugging.is_finite', 'is_finite')
  def is_finite(x, name=None):
    r"""Returns which elements of x are finite.
  
    @compatibility(numpy)
    Equivalent to np.isfinite
    @end_compatibility
  
    Example:
  
    ```python
    x = tf.constant([5.0, 4.8, 6.8, np.inf, np.nan])
    tf.math.is_finite(x) ==> [True, True, True, False, False]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "IsFinite", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return is_finite_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              is_finite, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "IsFinite", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            is_finite, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "IsFinite", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  IsFinite = tf_export("raw_ops.IsFinite")(_ops.to_raw_op(is_finite))
  
  
  def is_finite_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"IsFinite", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "IsFinite", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.is_inf', v1=['math.is_inf', 'debugging.is_inf', 'is_inf'])
  @deprecated_endpoints('debugging.is_inf', 'is_inf')
  def is_inf(x, name=None):
    r"""Returns which elements of x are Inf.
  
    @compatibility(numpy)
    Equivalent to np.isinf
    @end_compatibility
  
    Example:
  
    ```python
    x = tf.constant([5.0, np.inf, 6.8, np.inf])
    tf.math.is_inf(x) ==> [False, True, False, True]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "IsInf", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return is_inf_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              is_inf, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "IsInf", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            is_inf, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "IsInf", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  IsInf = tf_export("raw_ops.IsInf")(_ops.to_raw_op(is_inf))
  
  
  def is_inf_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"IsInf", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "IsInf", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.is_nan', v1=['math.is_nan', 'debugging.is_nan', 'is_nan'])
  @deprecated_endpoints('debugging.is_nan', 'is_nan')
  def is_nan(x, name=None):
    r"""Returns which elements of x are NaN.
  
    @compatibility(numpy)
    Equivalent to np.isnan
    @end_compatibility
  
    Example:
  
    ```python
    x = tf.constant([5.0, np.nan, 6.8, np.nan, np.inf])
    tf.math.is_nan(x) ==> [False, True, False, True, False]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "IsNan", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return is_nan_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              is_nan, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "IsNan", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            is_nan, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "IsNan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  IsNan = tf_export("raw_ops.IsNan")(_ops.to_raw_op(is_nan))
  
  
  def is_nan_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"IsNan", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "IsNan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.less', 'less')
  def less(x, y, name=None):
    r"""Returns the truth value of (x < y) element-wise.
  
    *NOTE*: `math.less` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Example:
  
    ```python
    x = tf.constant([5, 4, 6])
    y = tf.constant([5])
    tf.math.less(x, y) ==> [False, True, False]
  
    x = tf.constant([5, 4, 6])
    y = tf.constant([5, 6, 7])
    tf.math.less(x, y) ==> [False, True, True]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Less", name, tld.op_callbacks,
          x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return less_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              less, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Less", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            less, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Less", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Less = tf_export("raw_ops.Less")(_ops.to_raw_op(less))
  
  
  def less_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Less", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Less", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.less_equal', 'less_equal')
  def less_equal(x, y, name=None):
    r"""Returns the truth value of (x <= y) element-wise.
  
    *NOTE*: `math.less_equal` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Example:
  
    ```python
    x = tf.constant([5, 4, 6])
    y = tf.constant([5])
    tf.math.less_equal(x, y) ==> [True, True, False]
  
    x = tf.constant([5, 4, 6])
    y = tf.constant([5, 6, 6])
    tf.math.less_equal(x, y) ==> [True, True, True]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "LessEqual", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return less_equal_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              less_equal, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "LessEqual", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            less_equal, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "LessEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  LessEqual = tf_export("raw_ops.LessEqual")(_ops.to_raw_op(less_equal))
  
  
  def less_equal_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"LessEqual", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "LessEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.lgamma', v1=['math.lgamma', 'lgamma'])
  @deprecated_endpoints('lgamma')
  def lgamma(x, name=None):
    r"""Computes the log of the absolute value of `Gamma(x)` element-wise.
  
      For positive numbers, this function computes log((input - 1)!) for every element in the tensor.
      `lgamma(5) = log((5-1)!) = log(4!) = log(24) = 3.1780539`
  
    Example:
  
    ```python
    x = tf.constant([0, 0.5, 1, 4.5, -4, -5.6])
    tf.math.lgamma(x) ==> [inf, 0.5723649, 0., 2.4537368, inf, -4.6477685]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Lgamma", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return lgamma_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              lgamma, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Lgamma", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            lgamma, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Lgamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Lgamma = tf_export("raw_ops.Lgamma")(_ops.to_raw_op(lgamma))
  
  
  def lgamma_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Lgamma", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Lgamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def lin_space(start, stop, num, name=None):
    r"""Generates values in an interval.
  
    A sequence of `num` evenly-spaced values are generated beginning at `start`.
    If `num > 1`, the values in the sequence increase by `stop - start / num - 1`,
    so that the last one is exactly `stop`.
  
    For example:
  
    ```
    tf.linspace(10.0, 12.0, 3, name="linspace") => [ 10.0  11.0  12.0]
    ```
  
    Args:
      start: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
        0-D tensor. First entry in the range.
      stop: A `Tensor`. Must have the same type as `start`.
        0-D tensor. Last entry in the range.
      num: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        0-D tensor. Number of values to generate.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `start`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "LinSpace", name,
          tld.op_callbacks, start, stop, num)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return lin_space_eager_fallback(
            start, stop, num, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "LinSpace", start=start, stop=stop, num=num, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "LinSpace", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  LinSpace = tf_export("raw_ops.LinSpace")(_ops.to_raw_op(lin_space))
  
  
  def lin_space_eager_fallback(start, stop, num, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([start, stop], ctx)
    (start, stop) = _inputs_T
    _attr_Tidx, (num,) = _execute.args_to_matching_eager([num], ctx, _dtypes.int32)
    _inputs_flat = [start, stop, num]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"LinSpace", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "LinSpace", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.log', v1=['math.log', 'log'])
  @deprecated_endpoints('log')
  def log(x, name=None):
    r"""Computes natural logarithm of x element-wise.
  
    I.e., \\(y = \log_e x\\).
  
    Example:
  
    ```python
    >>> x = tf.constant([0, 0.5, 1, 5])
    >>> tf.math.log(x)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=array([      -inf, -0.6931472,  0.       ,  1.609438 ], dtype=float32)>
  
    ```
  
    See: https://en.wikipedia.org/wiki/Logarithm
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Log", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return log_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              log, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Log", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            log, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Log", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Log = tf_export("raw_ops.Log")(_ops.to_raw_op(log))
  
  
  def log_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Log", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Log", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.log1p', v1=['math.log1p', 'log1p'])
  @deprecated_endpoints('log1p')
  def log1p(x, name=None):
    r"""Computes natural logarithm of (1 + x) element-wise.
  
    I.e., \\(y = \log_e (1 + x)\\).
  
    Example:
    >>> x = tf.constant([0, 0.5, 1, 5])
    >>> tf.math.log1p(x)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=array([0.       , 0.4054651, 0.6931472, 1.7917595], dtype=float32)>
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Log1p", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return log1p_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              log1p, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Log1p", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            log1p, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Log1p", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Log1p = tf_export("raw_ops.Log1p")(_ops.to_raw_op(log1p))
  
  
  def log1p_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Log1p", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Log1p", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def logical_and(x, y, name=None):
    r"""Returns the truth value of x AND y element-wise.
  
    *NOTE*: `LogicalAnd` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor` of type `bool`.
      y: A `Tensor` of type `bool`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "LogicalAnd", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return logical_and_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "LogicalAnd", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ()
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "LogicalAnd", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  LogicalAnd = tf_export("raw_ops.LogicalAnd")(_ops.to_raw_op(logical_and))
  
  
  def logical_and_eager_fallback(x, y, name, ctx):
    x = _ops.convert_to_tensor(x, _dtypes.bool)
    y = _ops.convert_to_tensor(y, _dtypes.bool)
    _inputs_flat = [x, y]
    _attrs = None
    _result = _execute.execute(b"LogicalAnd", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "LogicalAnd", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.logical_not', 'logical_not')
  def logical_not(x, name=None):
    r"""Returns the truth value of `NOT x` element-wise.
  
    Example:
  
    >>> tf.math.logical_not(tf.constant([True, False]))
    <tf.Tensor: shape=(2,), dtype=bool, numpy=array([False,  True])>
  
    Args:
      x: A `Tensor` of type `bool`. A `Tensor` of type `bool`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "LogicalNot", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return logical_not_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              logical_not, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "LogicalNot", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            logical_not, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ()
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "LogicalNot", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  LogicalNot = tf_export("raw_ops.LogicalNot")(_ops.to_raw_op(logical_not))
  
  
  def logical_not_eager_fallback(x, name, ctx):
    x = _ops.convert_to_tensor(x, _dtypes.bool)
    _inputs_flat = [x]
    _attrs = None
    _result = _execute.execute(b"LogicalNot", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "LogicalNot", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.logical_or', 'logical_or')
  def logical_or(x, y, name=None):
    r"""Returns the truth value of x OR y element-wise.
  
    *NOTE*: `math.logical_or` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor` of type `bool`.
      y: A `Tensor` of type `bool`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "LogicalOr", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return logical_or_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              logical_or, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "LogicalOr", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            logical_or, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ()
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "LogicalOr", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  LogicalOr = tf_export("raw_ops.LogicalOr")(_ops.to_raw_op(logical_or))
  
  
  def logical_or_eager_fallback(x, y, name, ctx):
    x = _ops.convert_to_tensor(x, _dtypes.bool)
    y = _ops.convert_to_tensor(y, _dtypes.bool)
    _inputs_flat = [x, y]
    _attrs = None
    _result = _execute.execute(b"LogicalOr", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "LogicalOr", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def mat_mul(a, b, transpose_a=False, transpose_b=False, name=None):
    r"""Multiply the matrix "a" by the matrix "b".
  
    The inputs must be two-dimensional matrices and the inner dimension of
    "a" (after being transposed if transpose_a is true) must match the
    outer dimension of "b" (after being transposed if transposed_b is
    true).
  
    *Note*: The default kernel implementation for MatMul on GPUs uses
    cublas.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
      b: A `Tensor`. Must have the same type as `a`.
      transpose_a: An optional `bool`. Defaults to `False`.
        If true, "a" is transposed before multiplication.
      transpose_b: An optional `bool`. Defaults to `False`.
        If true, "b" is transposed before multiplication.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `a`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "MatMul", name,
          tld.op_callbacks, a, b, "transpose_a", transpose_a, "transpose_b",
          transpose_b)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return mat_mul_eager_fallback(
            a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if transpose_a is None:
      transpose_a = False
    transpose_a = _execute.make_bool(transpose_a, "transpose_a")
    if transpose_b is None:
      transpose_b = False
    transpose_b = _execute.make_bool(transpose_b, "transpose_b")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "MatMul", a=a, b=b, transpose_a=transpose_a, transpose_b=transpose_b,
                    name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("transpose_a", _op._get_attr_bool("transpose_a"), "transpose_b",
                _op._get_attr_bool("transpose_b"), "T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "MatMul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  MatMul = tf_export("raw_ops.MatMul")(_ops.to_raw_op(mat_mul))
  
  
  def mat_mul_eager_fallback(a, b, transpose_a, transpose_b, name, ctx):
    if transpose_a is None:
      transpose_a = False
    transpose_a = _execute.make_bool(transpose_a, "transpose_a")
    if transpose_b is None:
      transpose_b = False
    transpose_b = _execute.make_bool(transpose_b, "transpose_b")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([a, b], ctx)
    (a, b) = _inputs_T
    _inputs_flat = [a, b]
    _attrs = ("transpose_a", transpose_a, "transpose_b", transpose_b, "T",
    _attr_T)
    _result = _execute.execute(b"MatMul", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "MatMul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _max(input, axis, keep_dims=False, name=None):
    r"""Computes the maximum of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Max", name, tld.op_callbacks,
          input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _max_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Max", input=input, reduction_indices=axis, keep_dims=keep_dims,
                 name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
                _op._get_attr_type("T"), "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Max", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Max = tf_export("raw_ops.Max")(_ops.to_raw_op(_max))
  
  
  def _max_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"Max", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Max", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.maximum', 'maximum')
  def maximum(x, y, name=None):
    r"""Returns the max of x and y (i.e. x > y ? x : y) element-wise.
  
    Example:
    >>> x = tf.constant([0., 0., 0., 0.])
    >>> y = tf.constant([-2., 0., 2., 5.])
    >>> tf.math.maximum(x, y)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=array([0., 0., 2., 5.], dtype=float32)>
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int16`, `int32`, `int64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Maximum", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return maximum_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              maximum, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Maximum", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            maximum, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Maximum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Maximum = tf_export("raw_ops.Maximum")(_ops.to_raw_op(maximum))
  
  
  def maximum_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Maximum", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Maximum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def mean(input, axis, keep_dims=False, name=None):
    r"""Computes the mean of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Mean", name, tld.op_callbacks,
          input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return mean_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Mean", input=input, reduction_indices=axis, keep_dims=keep_dims,
                  name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
                _op._get_attr_type("T"), "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Mean", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Mean = tf_export("raw_ops.Mean")(_ops.to_raw_op(mean))
  
  
  def mean_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"Mean", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Mean", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _min(input, axis, keep_dims=False, name=None):
    r"""Computes the minimum of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Min", name, tld.op_callbacks,
          input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _min_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Min", input=input, reduction_indices=axis, keep_dims=keep_dims,
                 name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
                _op._get_attr_type("T"), "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Min", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Min = tf_export("raw_ops.Min")(_ops.to_raw_op(_min))
  
  
  def _min_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"Min", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Min", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.minimum', 'minimum')
  def minimum(x, y, name=None):
    r"""Returns the min of x and y (i.e. x < y ? x : y) element-wise.
  
    Both inputs are number-type tensors (except complex).  `minimum` expects that
    both tensors have the same `dtype`.
  
    Examples:
  
    >>> x = tf.constant([0., 0., 0., 0.])
    >>> y = tf.constant([-5., -2., 0., 3.])
    >>> tf.math.minimum(x, y)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -2., 0., 0.], dtype=float32)>
  
    Note that `minimum` supports broadcast semantics.
  
    >>> x = tf.constant([-5., 0., 0., 0.])
    >>> y = tf.constant([-3.])
    >>> tf.math.minimum(x, y)
    <tf.Tensor: shape=(4,), dtype=float32, numpy=array([-5., -3., -3., -3.], dtype=float32)>
  
    If inputs are not tensors, they will be converted to tensors.  See
    `tf.convert_to_tensor`.
  
    >>> x = tf.constant([-3.], dtype=tf.float32)
    >>> tf.math.minimum([-5], x)
    <tf.Tensor: shape=(1,), dtype=float32, numpy=array([-5.], dtype=float32)>
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int16`, `int32`, `int64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Minimum", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return minimum_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              minimum, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Minimum", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            minimum, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Minimum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Minimum = tf_export("raw_ops.Minimum")(_ops.to_raw_op(minimum))
  
  
  def minimum_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Minimum", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Minimum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def mod(x, y, name=None):
    r"""Returns element-wise remainder of division. This emulates C semantics in that
  
    the result here is consistent with a truncating divide. E.g.
    `tf.truncatediv(x, y) * y + truncate_mod(x, y) = x`.
  
    *NOTE*: `Mod` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `int32`, `int64`, `half`, `half`, `bfloat16`, `float32`, `float64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Mod", name, tld.op_callbacks,
          x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return mod_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Mod", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Mod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Mod = tf_export("raw_ops.Mod")(_ops.to_raw_op(mod))
  
  
  def mod_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Mod", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Mod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def mul(x, y, name=None):
    r"""Returns x * y element-wise.
  
    *NOTE*: `Multiply` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Mul", name, tld.op_callbacks,
          x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return mul_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Mul", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Mul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Mul = tf_export("raw_ops.Mul")(_ops.to_raw_op(mul))
  
  
  def mul_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Mul", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Mul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def mul_no_nan(x, y, name=None):
    r"""Returns x * y element-wise. Returns zero if y is zero, even if x if infinite or NaN.
  
    *NOTE*: `MulNoNan` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "MulNoNan", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return mul_no_nan_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "MulNoNan", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "MulNoNan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  MulNoNan = tf_export("raw_ops.MulNoNan")(_ops.to_raw_op(mul_no_nan))
  
  
  def mul_no_nan_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"MulNoNan", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "MulNoNan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def ndtri(x, name=None):
    r"""TODO: add doc.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Ndtri", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return ndtri_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Ndtri", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Ndtri", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Ndtri = tf_export("raw_ops.Ndtri")(_ops.to_raw_op(ndtri))
  
  
  def ndtri_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Ndtri", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Ndtri", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.negative', 'negative')
  def neg(x, name=None):
    r"""Computes numerical negative value element-wise.
  
    I.e., \\(y = -x\\).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Neg", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return neg_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              neg, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Neg", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            neg, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Neg", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Neg = tf_export("raw_ops.Neg")(_ops.to_raw_op(neg))
  
  
  def neg_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Neg", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Neg", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.nextafter')
  def next_after(x1, x2, name=None):
    r"""Returns the next representable value of `x1` in the direction of `x2`, element-wise.
  
    This operation returns the same result as the C++ std::nextafter function.
  
    It can also return a subnormal number.
  
    @compatibility(cpp)
    Equivalent to C++ std::nextafter function.
    @end_compatibility
  
    Args:
      x1: A `Tensor`. Must be one of the following types: `float64`, `float32`.
      x2: A `Tensor`. Must have the same type as `x1`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x1`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "NextAfter", name,
          tld.op_callbacks, x1, x2)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return next_after_eager_fallback(
            x1, x2, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              next_after, (), dict(x1=x1, x2=x2, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "NextAfter", x1=x1, x2=x2, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            next_after, (), dict(x1=x1, x2=x2, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "NextAfter", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  NextAfter = tf_export("raw_ops.NextAfter")(_ops.to_raw_op(next_after))
  
  
  def next_after_eager_fallback(x1, x2, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x1, x2], ctx, _dtypes.float32)
    (x1, x2) = _inputs_T
    _inputs_flat = [x1, x2]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"NextAfter", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "NextAfter", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def not_equal(x, y, incompatible_shape_error=True, name=None):
    r"""Returns the truth value of (x != y) element-wise.
  
    *NOTE*: `NotEqual` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `int16`, `int32`, `int64`, `uint16`, `uint32`, `uint64`, `complex64`, `quint8`, `qint8`, `qint32`, `string`, `bool`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      incompatible_shape_error: An optional `bool`. Defaults to `True`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `bool`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "NotEqual", name,
          tld.op_callbacks, x, y, "incompatible_shape_error",
          incompatible_shape_error)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return not_equal_eager_fallback(
            x, y, incompatible_shape_error=incompatible_shape_error, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if incompatible_shape_error is None:
      incompatible_shape_error = True
    incompatible_shape_error = _execute.make_bool(incompatible_shape_error, "incompatible_shape_error")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "NotEqual", x=x, y=y,
                      incompatible_shape_error=incompatible_shape_error,
                      name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "incompatible_shape_error",
                _op._get_attr_bool("incompatible_shape_error"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "NotEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  NotEqual = tf_export("raw_ops.NotEqual")(_ops.to_raw_op(not_equal))
  
  
  def not_equal_eager_fallback(x, y, incompatible_shape_error, name, ctx):
    if incompatible_shape_error is None:
      incompatible_shape_error = True
    incompatible_shape_error = _execute.make_bool(incompatible_shape_error, "incompatible_shape_error")
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T, "incompatible_shape_error",
    incompatible_shape_error)
    _result = _execute.execute(b"NotEqual", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "NotEqual", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.polygamma', v1=['math.polygamma', 'polygamma'])
  @deprecated_endpoints('polygamma')
  def polygamma(a, x, name=None):
    r"""Compute the polygamma function \\(\psi^{(n)}(x)\\).
  
    The polygamma function is defined as:
  
  
    \\(\psi^{(a)}(x) = \frac{d^a}{dx^a} \psi(x)\\)
  
    where \\(\psi(x)\\) is the digamma function.
    The polygamma function is defined only for non-negative integer orders \\a\\.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `float32`, `float64`.
      x: A `Tensor`. Must have the same type as `a`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `a`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Polygamma", name,
          tld.op_callbacks, a, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return polygamma_eager_fallback(
            a, x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              polygamma, (), dict(a=a, x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Polygamma", a=a, x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            polygamma, (), dict(a=a, x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Polygamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Polygamma = tf_export("raw_ops.Polygamma")(_ops.to_raw_op(polygamma))
  
  
  def polygamma_eager_fallback(a, x, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([a, x], ctx)
    (a, x) = _inputs_T
    _inputs_flat = [a, x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Polygamma", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Polygamma", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _pow(x, y, name=None):
    r"""Computes the power of one value to another.
  
    Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
    corresponding elements in `x` and `y`. For example:
  
    ```
    # tensor 'x' is [[2, 2]], [3, 3]]
    # tensor 'y' is [[8, 16], [2, 3]]
    tf.pow(x, y) ==> [[256, 65536], [9, 27]]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `float32`, `half`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Pow", name, tld.op_callbacks,
          x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _pow_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Pow", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Pow", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Pow = tf_export("raw_ops.Pow")(_ops.to_raw_op(_pow))
  
  
  def _pow_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Pow", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Pow", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def prod(input, axis, keep_dims=False, name=None):
    r"""Computes the product of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Prod", name, tld.op_callbacks,
          input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return prod_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Prod", input=input, reduction_indices=axis, keep_dims=keep_dims,
                  name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
                _op._get_attr_type("T"), "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Prod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Prod = tf_export("raw_ops.Prod")(_ops.to_raw_op(prod))
  
  
  def prod_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"Prod", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Prod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  _QuantizeDownAndShrinkRangeOutput = collections.namedtuple(
      "QuantizeDownAndShrinkRange",
      ["output", "output_min", "output_max"])
  
  
  def quantize_down_and_shrink_range(input, input_min, input_max, out_type, name=None):
    r"""Convert the quantized 'input' tensor into a lower-precision 'output', using the
  
    actual distribution of the values to maximize the usage of the lower bit depth
    and adjusting the output min and max ranges accordingly.
  
    [input_min, input_max] are scalar floats that specify the range for the float
    interpretation of the 'input' data. For example, if input_min is -1.0f and
    input_max is 1.0f, and we are dealing with quint16 quantized data, then a 0
    value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
  
    This operator tries to squeeze as much precision as possible into an output with
    a lower bit depth by calculating the actual min and max values found in the
    data. For example, maybe that quint16 input has no values lower than 16,384 and
    none higher than 49,152. That means only half the range is actually needed, all
    the float interpretations are between -0.5f and 0.5f, so if we want to compress
    the data into a quint8 output, we can use that range rather than the theoretical
    -1.0f to 1.0f that is suggested by the input min and max.
  
    In practice, this is most useful for taking output from operations like
    QuantizedMatMul that can produce higher bit-depth outputs than their inputs and
    may have large potential output ranges, but in practice have a distribution of
    input values that only uses a small fraction of the possible range. By feeding
    that output into this operator, we can reduce it from 32 bits down to 8 with
    minimal loss of accuracy.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
      input_min: A `Tensor` of type `float32`.
        The float value that the minimum quantized input value represents.
      input_max: A `Tensor` of type `float32`.
        The float value that the maximum quantized input value represents.
      out_type: A `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`.
        The type of the output. Should be a lower bit depth than Tinput.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (output, output_min, output_max).
  
      output: A `Tensor` of type `out_type`.
      output_min: A `Tensor` of type `float32`.
      output_max: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "QuantizeDownAndShrinkRange",
          name, tld.op_callbacks, input, input_min, input_max, "out_type",
          out_type)
        _result = _QuantizeDownAndShrinkRangeOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return quantize_down_and_shrink_range_eager_fallback(
            input, input_min, input_max, out_type=out_type, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    out_type = _execute.make_type(out_type, "out_type")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "QuantizeDownAndShrinkRange", input=input, input_min=input_min,
                                        input_max=input_max, out_type=out_type,
                                        name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("Tinput", _op._get_attr_type("Tinput"), "out_type",
                _op._get_attr_type("out_type"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "QuantizeDownAndShrinkRange", _inputs_flat, _attrs, _result)
    _result = _QuantizeDownAndShrinkRangeOutput._make(_result)
    return _result
  
  QuantizeDownAndShrinkRange = tf_export("raw_ops.QuantizeDownAndShrinkRange")(_ops.to_raw_op(quantize_down_and_shrink_range))
  
  
  def quantize_down_and_shrink_range_eager_fallback(input, input_min, input_max, out_type, name, ctx):
    out_type = _execute.make_type(out_type, "out_type")
    _attr_Tinput, (input,) = _execute.args_to_matching_eager([input], ctx)
    input_min = _ops.convert_to_tensor(input_min, _dtypes.float32)
    input_max = _ops.convert_to_tensor(input_max, _dtypes.float32)
    _inputs_flat = [input, input_min, input_max]
    _attrs = ("Tinput", _attr_Tinput, "out_type", out_type)
    _result = _execute.execute(b"QuantizeDownAndShrinkRange", 3,
                               inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
                               name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "QuantizeDownAndShrinkRange", _inputs_flat, _attrs, _result)
    _result = _QuantizeDownAndShrinkRangeOutput._make(_result)
    return _result
  
  _QuantizedAddOutput = collections.namedtuple(
      "QuantizedAdd",
      ["z", "min_z", "max_z"])
  
  
  def quantized_add(x, y, min_x, max_x, min_y, max_y, Toutput=_dtypes.qint32, name=None):
    r"""Returns x + y element-wise, working on quantized buffers.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
      y: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
      min_x: A `Tensor` of type `float32`.
        The float value that the lowest quantized `x` value represents.
      max_x: A `Tensor` of type `float32`.
        The float value that the highest quantized `x` value represents.
      min_y: A `Tensor` of type `float32`.
        The float value that the lowest quantized `y` value represents.
      max_y: A `Tensor` of type `float32`.
        The float value that the highest quantized `y` value represents.
      Toutput: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (z, min_z, max_z).
  
      z: A `Tensor` of type `Toutput`.
      min_z: A `Tensor` of type `float32`.
      max_z: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "QuantizedAdd", name,
          tld.op_callbacks, x, y, min_x, max_x, min_y, max_y, "Toutput",
          Toutput)
        _result = _QuantizedAddOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return quantized_add_eager_fallback(
            x, y, min_x, max_x, min_y, max_y, Toutput=Toutput, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Toutput is None:
      Toutput = _dtypes.qint32
    Toutput = _execute.make_type(Toutput, "Toutput")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "QuantizedAdd", x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y,
                          max_y=max_y, Toutput=Toutput, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T1", _op._get_attr_type("T1"), "T2", _op._get_attr_type("T2"),
                "Toutput", _op._get_attr_type("Toutput"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "QuantizedAdd", _inputs_flat, _attrs, _result)
    _result = _QuantizedAddOutput._make(_result)
    return _result
  
  QuantizedAdd = tf_export("raw_ops.QuantizedAdd")(_ops.to_raw_op(quantized_add))
  
  
  def quantized_add_eager_fallback(x, y, min_x, max_x, min_y, max_y, Toutput, name, ctx):
    if Toutput is None:
      Toutput = _dtypes.qint32
    Toutput = _execute.make_type(Toutput, "Toutput")
    _attr_T1, (x,) = _execute.args_to_matching_eager([x], ctx)
    _attr_T2, (y,) = _execute.args_to_matching_eager([y], ctx)
    min_x = _ops.convert_to_tensor(min_x, _dtypes.float32)
    max_x = _ops.convert_to_tensor(max_x, _dtypes.float32)
    min_y = _ops.convert_to_tensor(min_y, _dtypes.float32)
    max_y = _ops.convert_to_tensor(max_y, _dtypes.float32)
    _inputs_flat = [x, y, min_x, max_x, min_y, max_y]
    _attrs = ("T1", _attr_T1, "T2", _attr_T2, "Toutput", Toutput)
    _result = _execute.execute(b"QuantizedAdd", 3, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "QuantizedAdd", _inputs_flat, _attrs, _result)
    _result = _QuantizedAddOutput._make(_result)
    return _result
  
  _QuantizedMatMulOutput = collections.namedtuple(
      "QuantizedMatMul",
      ["out", "min_out", "max_out"])
  
  
  def quantized_mat_mul(a, b, min_a, max_a, min_b, max_b, Toutput=_dtypes.qint32, transpose_a=False, transpose_b=False, Tactivation=_dtypes.quint8, name=None):
    r"""Perform a quantized matrix multiplication of  `a` by the matrix `b`.
  
    The inputs must be two-dimensional matrices and the inner dimension of
    `a` (after being transposed if `transpose_a` is non-zero) must match the
    outer dimension of `b` (after being transposed if `transposed_b` is
    non-zero).
  
    Args:
      a: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
        Must be a two-dimensional tensor.
      b: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
        Must be a two-dimensional tensor.
      min_a: A `Tensor` of type `float32`.
        The float value that the lowest quantized `a` value represents.
      max_a: A `Tensor` of type `float32`.
        The float value that the highest quantized `a` value represents.
      min_b: A `Tensor` of type `float32`.
        The float value that the lowest quantized `b` value represents.
      max_b: A `Tensor` of type `float32`.
        The float value that the highest quantized `b` value represents.
      Toutput: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`.
      transpose_a: An optional `bool`. Defaults to `False`.
        If true, `a` is transposed before multiplication.
      transpose_b: An optional `bool`. Defaults to `False`.
        If true, `b` is transposed before multiplication.
      Tactivation: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`.
        The type of output produced by activation function
        following this operation.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (out, min_out, max_out).
  
      out: A `Tensor` of type `Toutput`.
      min_out: A `Tensor` of type `float32`.
      max_out: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "QuantizedMatMul", name,
          tld.op_callbacks, a, b, min_a, max_a, min_b, max_b, "Toutput",
          Toutput, "transpose_a", transpose_a, "transpose_b", transpose_b,
          "Tactivation", Tactivation)
        _result = _QuantizedMatMulOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return quantized_mat_mul_eager_fallback(
            a, b, min_a, max_a, min_b, max_b, Toutput=Toutput,
            transpose_a=transpose_a, transpose_b=transpose_b,
            Tactivation=Tactivation, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Toutput is None:
      Toutput = _dtypes.qint32
    Toutput = _execute.make_type(Toutput, "Toutput")
    if transpose_a is None:
      transpose_a = False
    transpose_a = _execute.make_bool(transpose_a, "transpose_a")
    if transpose_b is None:
      transpose_b = False
    transpose_b = _execute.make_bool(transpose_b, "transpose_b")
    if Tactivation is None:
      Tactivation = _dtypes.quint8
    Tactivation = _execute.make_type(Tactivation, "Tactivation")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "QuantizedMatMul", a=a, b=b, min_a=min_a, max_a=max_a, min_b=min_b,
                             max_b=max_b, Toutput=Toutput,
                             transpose_a=transpose_a, transpose_b=transpose_b,
                             Tactivation=Tactivation, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T1", _op._get_attr_type("T1"), "T2", _op._get_attr_type("T2"),
                "Toutput", _op._get_attr_type("Toutput"), "transpose_a",
                _op._get_attr_bool("transpose_a"), "transpose_b",
                _op._get_attr_bool("transpose_b"), "Tactivation",
                _op._get_attr_type("Tactivation"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "QuantizedMatMul", _inputs_flat, _attrs, _result)
    _result = _QuantizedMatMulOutput._make(_result)
    return _result
  
  QuantizedMatMul = tf_export("raw_ops.QuantizedMatMul")(_ops.to_raw_op(quantized_mat_mul))
  
  
  def quantized_mat_mul_eager_fallback(a, b, min_a, max_a, min_b, max_b, Toutput, transpose_a, transpose_b, Tactivation, name, ctx):
    if Toutput is None:
      Toutput = _dtypes.qint32
    Toutput = _execute.make_type(Toutput, "Toutput")
    if transpose_a is None:
      transpose_a = False
    transpose_a = _execute.make_bool(transpose_a, "transpose_a")
    if transpose_b is None:
      transpose_b = False
    transpose_b = _execute.make_bool(transpose_b, "transpose_b")
    if Tactivation is None:
      Tactivation = _dtypes.quint8
    Tactivation = _execute.make_type(Tactivation, "Tactivation")
    _attr_T1, (a,) = _execute.args_to_matching_eager([a], ctx)
    _attr_T2, (b,) = _execute.args_to_matching_eager([b], ctx)
    min_a = _ops.convert_to_tensor(min_a, _dtypes.float32)
    max_a = _ops.convert_to_tensor(max_a, _dtypes.float32)
    min_b = _ops.convert_to_tensor(min_b, _dtypes.float32)
    max_b = _ops.convert_to_tensor(max_b, _dtypes.float32)
    _inputs_flat = [a, b, min_a, max_a, min_b, max_b]
    _attrs = ("T1", _attr_T1, "T2", _attr_T2, "Toutput", Toutput, "transpose_a",
    transpose_a, "transpose_b", transpose_b, "Tactivation", Tactivation)
    _result = _execute.execute(b"QuantizedMatMul", 3, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "QuantizedMatMul", _inputs_flat, _attrs, _result)
    _result = _QuantizedMatMulOutput._make(_result)
    return _result
  
  _QuantizedMulOutput = collections.namedtuple(
      "QuantizedMul",
      ["z", "min_z", "max_z"])
  
  
  def quantized_mul(x, y, min_x, max_x, min_y, max_y, Toutput=_dtypes.qint32, name=None):
    r"""Returns x * y element-wise, working on quantized buffers.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
      y: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
      min_x: A `Tensor` of type `float32`.
        The float value that the lowest quantized `x` value represents.
      max_x: A `Tensor` of type `float32`.
        The float value that the highest quantized `x` value represents.
      min_y: A `Tensor` of type `float32`.
        The float value that the lowest quantized `y` value represents.
      max_y: A `Tensor` of type `float32`.
        The float value that the highest quantized `y` value represents.
      Toutput: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.qint32`.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (z, min_z, max_z).
  
      z: A `Tensor` of type `Toutput`.
      min_z: A `Tensor` of type `float32`.
      max_z: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "QuantizedMul", name,
          tld.op_callbacks, x, y, min_x, max_x, min_y, max_y, "Toutput",
          Toutput)
        _result = _QuantizedMulOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return quantized_mul_eager_fallback(
            x, y, min_x, max_x, min_y, max_y, Toutput=Toutput, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Toutput is None:
      Toutput = _dtypes.qint32
    Toutput = _execute.make_type(Toutput, "Toutput")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "QuantizedMul", x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y,
                          max_y=max_y, Toutput=Toutput, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T1", _op._get_attr_type("T1"), "T2", _op._get_attr_type("T2"),
                "Toutput", _op._get_attr_type("Toutput"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "QuantizedMul", _inputs_flat, _attrs, _result)
    _result = _QuantizedMulOutput._make(_result)
    return _result
  
  QuantizedMul = tf_export("raw_ops.QuantizedMul")(_ops.to_raw_op(quantized_mul))
  
  
  def quantized_mul_eager_fallback(x, y, min_x, max_x, min_y, max_y, Toutput, name, ctx):
    if Toutput is None:
      Toutput = _dtypes.qint32
    Toutput = _execute.make_type(Toutput, "Toutput")
    _attr_T1, (x,) = _execute.args_to_matching_eager([x], ctx)
    _attr_T2, (y,) = _execute.args_to_matching_eager([y], ctx)
    min_x = _ops.convert_to_tensor(min_x, _dtypes.float32)
    max_x = _ops.convert_to_tensor(max_x, _dtypes.float32)
    min_y = _ops.convert_to_tensor(min_y, _dtypes.float32)
    max_y = _ops.convert_to_tensor(max_y, _dtypes.float32)
    _inputs_flat = [x, y, min_x, max_x, min_y, max_y]
    _attrs = ("T1", _attr_T1, "T2", _attr_T2, "Toutput", Toutput)
    _result = _execute.execute(b"QuantizedMul", 3, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "QuantizedMul", _inputs_flat, _attrs, _result)
    _result = _QuantizedMulOutput._make(_result)
    return _result
  
  
  def ragged_bincount(splits, values, size, weights, binary_output=False, name=None):
    r"""Counts the number of occurrences of each value in an integer array.
  
    Outputs a vector with length `size` and the same dtype as `weights`. If
    `weights` are empty, then index `i` stores the number of times the value `i` is
    counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
    the value in `weights` at each index where the corresponding value in `arr` is
    `i`.
  
    Values in `arr` outside of the range [0, size) are ignored.
  
    Args:
      splits: A `Tensor` of type `int64`. 1D int64 `Tensor`.
      values: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        2D int `Tensor`.
      size: A `Tensor`. Must have the same type as `values`.
        non-negative int scalar `Tensor`.
      weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
        is an int32, int64, float32, or float64 `Tensor` with the same
        shape as `input`, or a length-0 `Tensor`, in which case it acts as all weights
        equal to 1.
      binary_output: An optional `bool`. Defaults to `False`.
        bool; Whether the kernel should count the appearance or number of occurrences.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `weights`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "RaggedBincount", name,
          tld.op_callbacks, splits, values, size, weights, "binary_output",
          binary_output)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return ragged_bincount_eager_fallback(
            splits, values, size, weights, binary_output=binary_output,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if binary_output is None:
      binary_output = False
    binary_output = _execute.make_bool(binary_output, "binary_output")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "RaggedBincount", splits=splits, values=values, size=size,
                            weights=weights, binary_output=binary_output,
                            name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("Tidx", _op._get_attr_type("Tidx"), "T",
                _op._get_attr_type("T"), "binary_output",
                _op._get_attr_bool("binary_output"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "RaggedBincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  RaggedBincount = tf_export("raw_ops.RaggedBincount")(_ops.to_raw_op(ragged_bincount))
  
  
  def ragged_bincount_eager_fallback(splits, values, size, weights, binary_output, name, ctx):
    if binary_output is None:
      binary_output = False
    binary_output = _execute.make_bool(binary_output, "binary_output")
    _attr_Tidx, _inputs_Tidx = _execute.args_to_matching_eager([values, size], ctx)
    (values, size) = _inputs_Tidx
    _attr_T, (weights,) = _execute.args_to_matching_eager([weights], ctx)
    splits = _ops.convert_to_tensor(splits, _dtypes.int64)
    _inputs_flat = [splits, values, size, weights]
    _attrs = ("Tidx", _attr_Tidx, "T", _attr_T, "binary_output", binary_output)
    _result = _execute.execute(b"RaggedBincount", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "RaggedBincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _range(start, limit, delta, name=None):
    r"""Creates a sequence of numbers.
  
    This operation creates a sequence of numbers that begins at `start` and
    extends by increments of `delta` up to but not including `limit`.
  
    For example:
  
    ```
    # 'start' is 3
    # 'limit' is 18
    # 'delta' is 3
    tf.range(start, limit, delta) ==> [3, 6, 9, 12, 15]
    ```
  
    Args:
      start: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`.
        0-D (scalar). First entry in the sequence.
      limit: A `Tensor`. Must have the same type as `start`.
        0-D (scalar). Upper limit of sequence, exclusive.
      delta: A `Tensor`. Must have the same type as `start`.
        0-D (scalar). Optional. Default is 1. Number that increments `start`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `start`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Range", name,
          tld.op_callbacks, start, limit, delta)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _range_eager_fallback(
            start, limit, delta, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Range", start=start, limit=limit, delta=delta, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Range", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Range = tf_export("raw_ops.Range")(_ops.to_raw_op(_range))
  
  
  def _range_eager_fallback(start, limit, delta, name, ctx):
    _attr_Tidx, _inputs_Tidx = _execute.args_to_matching_eager([start, limit, delta], ctx, _dtypes.int32)
    (start, limit, delta) = _inputs_Tidx
    _inputs_flat = [start, limit, delta]
    _attrs = ("Tidx", _attr_Tidx)
    _result = _execute.execute(b"Range", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Range", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def real(input, Tout=_dtypes.float32, name=None):
    r"""Returns the real part of a complex number.
  
    Given a tensor `input` of complex numbers, this operation returns a tensor of
    type `float` that is the real part of each element in `input`. All elements in
    `input` must be complex numbers of the form \\(a + bj\\), where *a* is the real
     part returned by this operation and *b* is the imaginary part.
  
    For example:
  
    ```
    # tensor 'input' is [-2.25 + 4.75j, 3.25 + 5.75j]
    tf.real(input) ==> [-2.25, 3.25]
    ```
  
    Args:
      input: A `Tensor`. Must be one of the following types: `complex64`, `complex128`.
      Tout: An optional `tf.DType` from: `tf.float32, tf.float64`. Defaults to `tf.float32`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `Tout`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Real", name, tld.op_callbacks,
          input, "Tout", Tout)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return real_eager_fallback(
            input, Tout=Tout, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Real", input=input, Tout=Tout, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tout",
                _op._get_attr_type("Tout"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Real", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Real = tf_export("raw_ops.Real")(_ops.to_raw_op(real))
  
  
  def real_eager_fallback(input, Tout, name, ctx):
    if Tout is None:
      Tout = _dtypes.float32
    Tout = _execute.make_type(Tout, "Tout")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, _dtypes.complex64)
    _inputs_flat = [input]
    _attrs = ("T", _attr_T, "Tout", Tout)
    _result = _execute.execute(b"Real", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Real", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('realdiv')
  def real_div(x, y, name=None):
    r"""Returns x / y element-wise for real types.
  
    If `x` and `y` are reals, this will return the floating-point division.
  
    *NOTE*: `Div` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "RealDiv", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return real_div_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              real_div, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "RealDiv", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            real_div, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "RealDiv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  RealDiv = tf_export("raw_ops.RealDiv")(_ops.to_raw_op(real_div))
  
  
  def real_div_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"RealDiv", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "RealDiv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.reciprocal', v1=['math.reciprocal', 'reciprocal'])
  @deprecated_endpoints('reciprocal')
  def reciprocal(x, name=None):
    r"""Computes the reciprocal of x element-wise.
  
    I.e., \\(y = 1 / x\\).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Reciprocal", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return reciprocal_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              reciprocal, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Reciprocal", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            reciprocal, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Reciprocal", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Reciprocal = tf_export("raw_ops.Reciprocal")(_ops.to_raw_op(reciprocal))
  
  
  def reciprocal_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Reciprocal", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Reciprocal", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def reciprocal_grad(y, dy, name=None):
    r"""Computes the gradient for the inverse of `x` wrt its input.
  
    Specifically, `grad = -dy * y*y`, where `y = 1/x`, and `dy`
    is the corresponding input gradient.
  
    Args:
      y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      dy: A `Tensor`. Must have the same type as `y`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `y`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "ReciprocalGrad", name,
          tld.op_callbacks, y, dy)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return reciprocal_grad_eager_fallback(
            y, dy, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "ReciprocalGrad", y=y, dy=dy, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "ReciprocalGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  ReciprocalGrad = tf_export("raw_ops.ReciprocalGrad")(_ops.to_raw_op(reciprocal_grad))
  
  
  def reciprocal_grad_eager_fallback(y, dy, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([y, dy], ctx)
    (y, dy) = _inputs_T
    _inputs_flat = [y, dy]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"ReciprocalGrad", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "ReciprocalGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  _RequantizationRangeOutput = collections.namedtuple(
      "RequantizationRange",
      ["output_min", "output_max"])
  
  
  def requantization_range(input, input_min, input_max, name=None):
    r"""Computes a range that covers the actual values present in a quantized tensor.
  
    Given a quantized tensor described by `(input, input_min, input_max)`, outputs a
    range that covers the actual values present in that tensor. This op is typically
    used to produce the `requested_output_min` and `requested_output_max` for
    `Requantize`.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
      input_min: A `Tensor` of type `float32`.
        The float value that the minimum quantized input value represents.
      input_max: A `Tensor` of type `float32`.
        The float value that the maximum quantized input value represents.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (output_min, output_max).
  
      output_min: A `Tensor` of type `float32`.
      output_max: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "RequantizationRange", name,
          tld.op_callbacks, input, input_min, input_max)
        _result = _RequantizationRangeOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return requantization_range_eager_fallback(
            input, input_min, input_max, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "RequantizationRange", input=input, input_min=input_min,
                                 input_max=input_max, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("Tinput", _op._get_attr_type("Tinput"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "RequantizationRange", _inputs_flat, _attrs, _result)
    _result = _RequantizationRangeOutput._make(_result)
    return _result
  
  RequantizationRange = tf_export("raw_ops.RequantizationRange")(_ops.to_raw_op(requantization_range))
  
  
  def requantization_range_eager_fallback(input, input_min, input_max, name, ctx):
    _attr_Tinput, (input,) = _execute.args_to_matching_eager([input], ctx)
    input_min = _ops.convert_to_tensor(input_min, _dtypes.float32)
    input_max = _ops.convert_to_tensor(input_max, _dtypes.float32)
    _inputs_flat = [input, input_min, input_max]
    _attrs = ("Tinput", _attr_Tinput)
    _result = _execute.execute(b"RequantizationRange", 2, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "RequantizationRange", _inputs_flat, _attrs, _result)
    _result = _RequantizationRangeOutput._make(_result)
    return _result
  
  _RequantizationRangePerChannelOutput = collections.namedtuple(
      "RequantizationRangePerChannel",
      ["output_min", "output_max"])
  
  
  def requantization_range_per_channel(input, input_min, input_max, clip_value_max, name=None):
    r"""Computes requantization range per channel.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
        The original input tensor.
      input_min: A `Tensor` of type `float32`.
        The minimum value of the input tensor
      input_max: A `Tensor` of type `float32`.
        The maximum value of the input tensor.
      clip_value_max: A `float`.
        The maximum value of the output that needs to be clipped.
        Example: set this to 6 for Relu6.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (output_min, output_max).
  
      output_min: A `Tensor` of type `float32`.
      output_max: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name,
          "RequantizationRangePerChannel", name, tld.op_callbacks, input,
          input_min, input_max, "clip_value_max", clip_value_max)
        _result = _RequantizationRangePerChannelOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return requantization_range_per_channel_eager_fallback(
            input, input_min, input_max, clip_value_max=clip_value_max,
            name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    clip_value_max = _execute.make_float(clip_value_max, "clip_value_max")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "RequantizationRangePerChannel", input=input, input_min=input_min,
                                           input_max=input_max,
                                           clip_value_max=clip_value_max,
                                           name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "clip_value_max",
                _op.get_attr("clip_value_max"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "RequantizationRangePerChannel", _inputs_flat, _attrs, _result)
    _result = _RequantizationRangePerChannelOutput._make(_result)
    return _result
  
  RequantizationRangePerChannel = tf_export("raw_ops.RequantizationRangePerChannel")(_ops.to_raw_op(requantization_range_per_channel))
  
  
  def requantization_range_per_channel_eager_fallback(input, input_min, input_max, clip_value_max, name, ctx):
    clip_value_max = _execute.make_float(clip_value_max, "clip_value_max")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, _dtypes.qint32)
    input_min = _ops.convert_to_tensor(input_min, _dtypes.float32)
    input_max = _ops.convert_to_tensor(input_max, _dtypes.float32)
    _inputs_flat = [input, input_min, input_max]
    _attrs = ("T", _attr_T, "clip_value_max", clip_value_max)
    _result = _execute.execute(b"RequantizationRangePerChannel", 2,
                               inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
                               name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "RequantizationRangePerChannel", _inputs_flat, _attrs, _result)
    _result = _RequantizationRangePerChannelOutput._make(_result)
    return _result
  
  _RequantizeOutput = collections.namedtuple(
      "Requantize",
      ["output", "output_min", "output_max"])
  
  
  def requantize(input, input_min, input_max, requested_output_min, requested_output_max, out_type, name=None):
    r"""Converts the quantized `input` tensor into a lower-precision `output`.
  
    Converts the quantized `input` tensor into a lower-precision `output`, using the
    output range specified with `requested_output_min` and `requested_output_max`.
  
    `[input_min, input_max]` are scalar floats that specify the range for the float
    interpretation of the `input` data. For example, if `input_min` is -1.0f and
    `input_max` is 1.0f, and we are dealing with `quint16` quantized data, then a 0
    value in the 16-bit data should be interpreted as -1.0f, and a 65535 means 1.0f.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
      input_min: A `Tensor` of type `float32`.
        The float value that the minimum quantized input value represents.
      input_max: A `Tensor` of type `float32`.
        The float value that the maximum quantized input value represents.
      requested_output_min: A `Tensor` of type `float32`.
        The float value that the minimum quantized output value represents.
      requested_output_max: A `Tensor` of type `float32`.
        The float value that the maximum quantized output value represents.
      out_type: A `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`.
        The type of the output. Should be a lower bit depth than Tinput.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (output, output_min, output_max).
  
      output: A `Tensor` of type `out_type`.
      output_min: A `Tensor` of type `float32`.
      output_max: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Requantize", name,
          tld.op_callbacks, input, input_min, input_max, requested_output_min,
          requested_output_max, "out_type", out_type)
        _result = _RequantizeOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return requantize_eager_fallback(
            input, input_min, input_max, requested_output_min,
            requested_output_max, out_type=out_type, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    out_type = _execute.make_type(out_type, "out_type")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Requantize", input=input, input_min=input_min, input_max=input_max,
                        requested_output_min=requested_output_min,
                        requested_output_max=requested_output_max,
                        out_type=out_type, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("Tinput", _op._get_attr_type("Tinput"), "out_type",
                _op._get_attr_type("out_type"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Requantize", _inputs_flat, _attrs, _result)
    _result = _RequantizeOutput._make(_result)
    return _result
  
  Requantize = tf_export("raw_ops.Requantize")(_ops.to_raw_op(requantize))
  
  
  def requantize_eager_fallback(input, input_min, input_max, requested_output_min, requested_output_max, out_type, name, ctx):
    out_type = _execute.make_type(out_type, "out_type")
    _attr_Tinput, (input,) = _execute.args_to_matching_eager([input], ctx)
    input_min = _ops.convert_to_tensor(input_min, _dtypes.float32)
    input_max = _ops.convert_to_tensor(input_max, _dtypes.float32)
    requested_output_min = _ops.convert_to_tensor(requested_output_min, _dtypes.float32)
    requested_output_max = _ops.convert_to_tensor(requested_output_max, _dtypes.float32)
    _inputs_flat = [input, input_min, input_max, requested_output_min, requested_output_max]
    _attrs = ("Tinput", _attr_Tinput, "out_type", out_type)
    _result = _execute.execute(b"Requantize", 3, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Requantize", _inputs_flat, _attrs, _result)
    _result = _RequantizeOutput._make(_result)
    return _result
  
  _RequantizePerChannelOutput = collections.namedtuple(
      "RequantizePerChannel",
      ["output", "output_min", "output_max"])
  
  
  def requantize_per_channel(input, input_min, input_max, requested_output_min, requested_output_max, out_type=_dtypes.quint8, name=None):
    r"""Requantizes input with min and max values known per channel.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`.
        The original input tensor.
      input_min: A `Tensor` of type `float32`.
        The minimum value of the input tensor
      input_max: A `Tensor` of type `float32`.
        The maximum value of the input tensor.
      requested_output_min: A `Tensor` of type `float32`.
        The minimum value of the output tensor requested.
      requested_output_max: A `Tensor` of type `float32`.
        The maximum value of the output tensor requested.
      out_type: An optional `tf.DType` from: `tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16`. Defaults to `tf.quint8`.
        The quantized type of output tensor that needs to be converted.
      name: A name for the operation (optional).
  
    Returns:
      A tuple of `Tensor` objects (output, output_min, output_max).
  
      output: A `Tensor` of type `out_type`.
      output_min: A `Tensor` of type `float32`.
      output_max: A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "RequantizePerChannel", name,
          tld.op_callbacks, input, input_min, input_max, requested_output_min,
          requested_output_max, "out_type", out_type)
        _result = _RequantizePerChannelOutput._make(_result)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return requantize_per_channel_eager_fallback(
            input, input_min, input_max, requested_output_min,
            requested_output_max, out_type=out_type, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if out_type is None:
      out_type = _dtypes.quint8
    out_type = _execute.make_type(out_type, "out_type")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "RequantizePerChannel", input=input, input_min=input_min,
                                  input_max=input_max,
                                  requested_output_min=requested_output_min,
                                  requested_output_max=requested_output_max,
                                  out_type=out_type, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "out_type",
                _op._get_attr_type("out_type"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "RequantizePerChannel", _inputs_flat, _attrs, _result)
    _result = _RequantizePerChannelOutput._make(_result)
    return _result
  
  RequantizePerChannel = tf_export("raw_ops.RequantizePerChannel")(_ops.to_raw_op(requantize_per_channel))
  
  
  def requantize_per_channel_eager_fallback(input, input_min, input_max, requested_output_min, requested_output_max, out_type, name, ctx):
    if out_type is None:
      out_type = _dtypes.quint8
    out_type = _execute.make_type(out_type, "out_type")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx, _dtypes.qint32)
    input_min = _ops.convert_to_tensor(input_min, _dtypes.float32)
    input_max = _ops.convert_to_tensor(input_max, _dtypes.float32)
    requested_output_min = _ops.convert_to_tensor(requested_output_min, _dtypes.float32)
    requested_output_max = _ops.convert_to_tensor(requested_output_max, _dtypes.float32)
    _inputs_flat = [input, input_min, input_max, requested_output_min, requested_output_max]
    _attrs = ("T", _attr_T, "out_type", out_type)
    _result = _execute.execute(b"RequantizePerChannel", 3, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "RequantizePerChannel", _inputs_flat, _attrs, _result)
    _result = _RequantizePerChannelOutput._make(_result)
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.rint', v1=['math.rint', 'rint'])
  @deprecated_endpoints('rint')
  def rint(x, name=None):
    r"""Returns element-wise integer closest to x.
  
    If the result is midway between two representable values,
    the even representable is chosen.
    For example:
  
    ```
    rint(-1.5) ==> -2.0
    rint(0.5000001) ==> 1.0
    rint([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0]) ==> [-2., -2., -0., 0., 2., 2., 2.]
    ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Rint", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return rint_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              rint, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Rint", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            rint, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Rint", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Rint = tf_export("raw_ops.Rint")(_ops.to_raw_op(rint))
  
  
  def rint_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Rint", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Rint", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def round(x, name=None):
    r"""Rounds the values of a tensor to the nearest integer, element-wise.
  
    Rounds half to even.  Also known as bankers rounding. If you want to round
    according to the current system rounding mode use std::cint.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Round", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return round_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Round", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Round", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Round = tf_export("raw_ops.Round")(_ops.to_raw_op(round))
  
  
  def round_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Round", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Round", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def rsqrt(x, name=None):
    r"""Computes reciprocal of square root of x element-wise.
  
    I.e., \\(y = 1 / \sqrt{x}\\).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Rsqrt", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return rsqrt_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Rsqrt", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Rsqrt", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Rsqrt = tf_export("raw_ops.Rsqrt")(_ops.to_raw_op(rsqrt))
  
  
  def rsqrt_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Rsqrt", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Rsqrt", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def rsqrt_grad(y, dy, name=None):
    r"""Computes the gradient for the rsqrt of `x` wrt its input.
  
    Specifically, `grad = dy * -0.5 * y^3`, where `y = rsqrt(x)`, and `dy`
    is the corresponding input gradient.
  
    Args:
      y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      dy: A `Tensor`. Must have the same type as `y`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `y`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "RsqrtGrad", name,
          tld.op_callbacks, y, dy)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return rsqrt_grad_eager_fallback(
            y, dy, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "RsqrtGrad", y=y, dy=dy, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "RsqrtGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  RsqrtGrad = tf_export("raw_ops.RsqrtGrad")(_ops.to_raw_op(rsqrt_grad))
  
  
  def rsqrt_grad_eager_fallback(y, dy, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([y, dy], ctx)
    (y, dy) = _inputs_T
    _inputs_flat = [y, dy]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"RsqrtGrad", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "RsqrtGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.segment_max', v1=['math.segment_max', 'segment_max'])
  @deprecated_endpoints('segment_max')
  def segment_max(data, segment_ids, name=None):
    r"""Computes the maximum along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Computes a tensor such that
    \\(output_i = \max_j(data_j)\\) where `max` is over `j` such
    that `segment_ids[j] == i`.
  
    If the max is empty for a given segment ID `i`, `output[i] = 0`.
  
    <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
    <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMax.png" alt>
    </div>
  
    For example:
  
    ```
    c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
    tf.segment_max(c, tf.constant([0, 0, 1]))
    # ==> [[4, 3, 3, 4],
    #      [5, 6, 7, 8]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor whose size is equal to the size of `data`'s
        first dimension.  Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SegmentMax", name,
          tld.op_callbacks, data, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return segment_max_eager_fallback(
            data, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              segment_max, (), dict(data=data, segment_ids=segment_ids,
                                    name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SegmentMax", data=data, segment_ids=segment_ids, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            segment_max, (), dict(data=data, segment_ids=segment_ids, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SegmentMax", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SegmentMax = tf_export("raw_ops.SegmentMax")(_ops.to_raw_op(segment_max))
  
  
  def segment_max_eager_fallback(data, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _inputs_flat = [data, segment_ids]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
    _result = _execute.execute(b"SegmentMax", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SegmentMax", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.segment_mean', v1=['math.segment_mean', 'segment_mean'])
  @deprecated_endpoints('segment_mean')
  def segment_mean(data, segment_ids, name=None):
    r"""Computes the mean along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Computes a tensor such that
    \\(output_i = \frac{\sum_j data_j}{N}\\) where `mean` is
    over `j` such that `segment_ids[j] == i` and `N` is the total number of
    values summed.
  
    If the mean is empty for a given segment ID `i`, `output[i] = 0`.
  
    <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
    <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMean.png" alt>
    </div>
  
    For example:
  
    ```
    c = tf.constant([[1.0,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
    tf.segment_mean(c, tf.constant([0, 0, 1]))
    # ==> [[2.5, 2.5, 2.5, 2.5],
    #      [5, 6, 7, 8]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor whose size is equal to the size of `data`'s
        first dimension.  Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SegmentMean", name,
          tld.op_callbacks, data, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return segment_mean_eager_fallback(
            data, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              segment_mean, (), dict(data=data, segment_ids=segment_ids,
                                     name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SegmentMean", data=data, segment_ids=segment_ids, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            segment_mean, (), dict(data=data, segment_ids=segment_ids,
                                   name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SegmentMean", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SegmentMean = tf_export("raw_ops.SegmentMean")(_ops.to_raw_op(segment_mean))
  
  
  def segment_mean_eager_fallback(data, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _inputs_flat = [data, segment_ids]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
    _result = _execute.execute(b"SegmentMean", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SegmentMean", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.segment_min', v1=['math.segment_min', 'segment_min'])
  @deprecated_endpoints('segment_min')
  def segment_min(data, segment_ids, name=None):
    r"""Computes the minimum along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Computes a tensor such that
    \\(output_i = \min_j(data_j)\\) where `min` is over `j` such
    that `segment_ids[j] == i`.
  
    If the min is empty for a given segment ID `i`, `output[i] = 0`.
  
    <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
    <img style="width:100%" src="https://www.tensorflow.org/images/SegmentMin.png" alt>
    </div>
  
    For example:
  
    ```
    c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
    tf.segment_min(c, tf.constant([0, 0, 1]))
    # ==> [[1, 2, 2, 1],
    #      [5, 6, 7, 8]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor whose size is equal to the size of `data`'s
        first dimension.  Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SegmentMin", name,
          tld.op_callbacks, data, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return segment_min_eager_fallback(
            data, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              segment_min, (), dict(data=data, segment_ids=segment_ids,
                                    name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SegmentMin", data=data, segment_ids=segment_ids, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            segment_min, (), dict(data=data, segment_ids=segment_ids, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SegmentMin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SegmentMin = tf_export("raw_ops.SegmentMin")(_ops.to_raw_op(segment_min))
  
  
  def segment_min_eager_fallback(data, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _inputs_flat = [data, segment_ids]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
    _result = _execute.execute(b"SegmentMin", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SegmentMin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.segment_prod', v1=['math.segment_prod', 'segment_prod'])
  @deprecated_endpoints('segment_prod')
  def segment_prod(data, segment_ids, name=None):
    r"""Computes the product along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Computes a tensor such that
    \\(output_i = \prod_j data_j\\) where the product is over `j` such
    that `segment_ids[j] == i`.
  
    If the product is empty for a given segment ID `i`, `output[i] = 1`.
  
    <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
    <img style="width:100%" src="https://www.tensorflow.org/images/SegmentProd.png" alt>
    </div>
  
    For example:
  
    ```
    c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
    tf.segment_prod(c, tf.constant([0, 0, 1]))
    # ==> [[4, 6, 6, 4],
    #      [5, 6, 7, 8]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor whose size is equal to the size of `data`'s
        first dimension.  Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SegmentProd", name,
          tld.op_callbacks, data, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return segment_prod_eager_fallback(
            data, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              segment_prod, (), dict(data=data, segment_ids=segment_ids,
                                     name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SegmentProd", data=data, segment_ids=segment_ids, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            segment_prod, (), dict(data=data, segment_ids=segment_ids,
                                   name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SegmentProd", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SegmentProd = tf_export("raw_ops.SegmentProd")(_ops.to_raw_op(segment_prod))
  
  
  def segment_prod_eager_fallback(data, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _inputs_flat = [data, segment_ids]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
    _result = _execute.execute(b"SegmentProd", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SegmentProd", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.segment_sum', v1=['math.segment_sum', 'segment_sum'])
  @deprecated_endpoints('segment_sum')
  def segment_sum(data, segment_ids, name=None):
    r"""Computes the sum along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Computes a tensor such that
    \\(output_i = \sum_j data_j\\) where sum is over `j` such
    that `segment_ids[j] == i`.
  
    If the sum is empty for a given segment ID `i`, `output[i] = 0`.
  
    <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
    <img style="width:100%" src="https://www.tensorflow.org/images/SegmentSum.png" alt>
    </div>
  
    For example:
  
    ```
    c = tf.constant([[1,2,3,4], [4, 3, 2, 1], [5,6,7,8]])
    tf.segment_sum(c, tf.constant([0, 0, 1]))
    # ==> [[5, 5, 5, 5],
    #      [5, 6, 7, 8]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor whose size is equal to the size of `data`'s
        first dimension.  Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SegmentSum", name,
          tld.op_callbacks, data, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return segment_sum_eager_fallback(
            data, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              segment_sum, (), dict(data=data, segment_ids=segment_ids,
                                    name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SegmentSum", data=data, segment_ids=segment_ids, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            segment_sum, (), dict(data=data, segment_ids=segment_ids, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SegmentSum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SegmentSum = tf_export("raw_ops.SegmentSum")(_ops.to_raw_op(segment_sum))
  
  
  def segment_sum_eager_fallback(data, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _inputs_flat = [data, segment_ids]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices)
    _result = _execute.execute(b"SegmentSum", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SegmentSum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def select(condition, x, y, name=None):
    r"""Selects elements from `x` or `y`, depending on `condition`.
  
    The `x`, and `y` tensors must all have the same shape, and the
    output will also have that shape.
  
    The `condition` tensor must be a scalar if `x` and `y` are scalars.
    If `x` and `y` are vectors or higher rank, then `condition` must be either a
    scalar, a vector with size matching the first dimension of `x`, or must have
    the same shape as `x`.
  
    The `condition` tensor acts as a mask that chooses, based on the value at each
    element, whether the corresponding element / row in the output should be
    taken from `x` (if true) or `y` (if false).
  
    If `condition` is a vector and `x` and `y` are higher rank matrices, then
    it chooses which row (outer dimension) to copy from `x` and `y`.
    If `condition` has the same shape as `x` and `y`, then it chooses which
    element to copy from `x` and `y`.
  
    For example:
  
    ```python
    # 'condition' tensor is [[True,  False]
    #                        [False, True]]
    # 't' is [[1, 2],
    #         [3, 4]]
    # 'e' is [[5, 6],
    #         [7, 8]]
    select(condition, t, e)  # => [[1, 6], [7, 4]]
  
  
    # 'condition' tensor is [True, False]
    # 't' is [[1, 2],
    #         [3, 4]]
    # 'e' is [[5, 6],
    #         [7, 8]]
    select(condition, t, e) ==> [[1, 2],
                                 [7, 8]]
  
    ```
  
    Args:
      condition: A `Tensor` of type `bool`.
      x:  A `Tensor` which may have the same shape as `condition`.
        If `condition` is rank 1, `x` may have higher rank,
        but its first dimension must match the size of `condition`.
      y:  A `Tensor` with the same type and shape as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `t`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Select", name,
          tld.op_callbacks, condition, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return select_eager_fallback(
            condition, x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Select", condition=condition, t=x, e=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Select", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Select = tf_export("raw_ops.Select")(_ops.to_raw_op(select))
  
  
  def select_eager_fallback(condition, x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    condition = _ops.convert_to_tensor(condition, _dtypes.bool)
    _inputs_flat = [condition, x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Select", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Select", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def select_v2(condition, t, e, name=None):
    r"""TODO: add doc.
  
    Args:
      condition: A `Tensor` of type `bool`.
      t: A `Tensor`.
      e: A `Tensor`. Must have the same type as `t`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `t`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SelectV2", name,
          tld.op_callbacks, condition, t, e)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return select_v2_eager_fallback(
            condition, t, e, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SelectV2", condition=condition, t=t, e=e, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SelectV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SelectV2 = tf_export("raw_ops.SelectV2")(_ops.to_raw_op(select_v2))
  
  
  def select_v2_eager_fallback(condition, t, e, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([t, e], ctx)
    (t, e) = _inputs_T
    condition = _ops.convert_to_tensor(condition, _dtypes.bool)
    _inputs_flat = [condition, t, e]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"SelectV2", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SelectV2", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sigmoid(x, name=None):
    r"""Computes sigmoid of `x` element-wise.
  
    Specifically, `y = 1 / (1 + exp(-x))`.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Sigmoid", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sigmoid_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Sigmoid", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Sigmoid", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Sigmoid = tf_export("raw_ops.Sigmoid")(_ops.to_raw_op(sigmoid))
  
  
  def sigmoid_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Sigmoid", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Sigmoid", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sigmoid_grad(y, dy, name=None):
    r"""Computes the gradient of the sigmoid of `x` wrt its input.
  
    Specifically, `grad = dy * y * (1 - y)`, where `y = sigmoid(x)`, and
    `dy` is the corresponding input gradient.
  
    Args:
      y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      dy: A `Tensor`. Must have the same type as `y`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `y`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SigmoidGrad", name,
          tld.op_callbacks, y, dy)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sigmoid_grad_eager_fallback(
            y, dy, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SigmoidGrad", y=y, dy=dy, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SigmoidGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SigmoidGrad = tf_export("raw_ops.SigmoidGrad")(_ops.to_raw_op(sigmoid_grad))
  
  
  def sigmoid_grad_eager_fallback(y, dy, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([y, dy], ctx)
    (y, dy) = _inputs_T
    _inputs_flat = [y, dy]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"SigmoidGrad", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SigmoidGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sign(x, name=None):
    r"""Returns an element-wise indication of the sign of a number.
  
    `y = sign(x) = -1` if `x < 0`; 0 if `x == 0`; 1 if `x > 0`.
  
    For complex numbers, `y = sign(x) = x / |x|` if `x != 0`, otherwise `y = 0`.
  
    Example usage:
    >>> tf.math.sign([0., 2., -3.])
    <tf.Tensor: shape=(3,), dtype=float32, numpy=array([ 0.,  1., -1.], dtype=float32)>
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Sign", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sign_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Sign", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Sign", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Sign = tf_export("raw_ops.Sign")(_ops.to_raw_op(sign))
  
  
  def sign_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Sign", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Sign", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.sin', 'sin')
  def sin(x, name=None):
    r"""Computes sine of x element-wise.
  
      Given an input tensor, this function computes sine of every
      element in the tensor. Input range is `(-inf, inf)` and
      output range is `[-1,1]`.
  
      ```python
      x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")])
      tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Sin", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sin_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              sin, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Sin", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            sin, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Sin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Sin = tf_export("raw_ops.Sin")(_ops.to_raw_op(sin))
  
  
  def sin_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Sin", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Sin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.sinh', 'sinh')
  def sinh(x, name=None):
    r"""Computes hyperbolic sine of x element-wise.
  
      Given an input tensor, this function computes hyperbolic sine of every
      element in the tensor. Input range is `[-inf,inf]` and output range
      is `[-inf,inf]`.
  
      ```python
      x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 2, 10, float("inf")])
      tf.math.sinh(x) ==> [-inf -4.0515420e+03 -5.2109528e-01 1.1752012e+00 1.5094614e+00 3.6268604e+00 1.1013232e+04 inf]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Sinh", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sinh_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              sinh, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Sinh", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            sinh, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Sinh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Sinh = tf_export("raw_ops.Sinh")(_ops.to_raw_op(sinh))
  
  
  def sinh_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Sinh", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Sinh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sobol_sample(dim, num_results, skip, dtype=_dtypes.float32, name=None):
    r"""Generates points from the Sobol sequence.
  
    Creates a Sobol sequence with `num_results` samples. Each sample has dimension
    `dim`. Skips the first `skip` samples.
  
    Args:
      dim: A `Tensor` of type `int32`.
        Positive scalar `Tensor` representing each sample's dimension.
      num_results: A `Tensor` of type `int32`.
        Positive scalar `Tensor` of dtype int32. The number of Sobol points to return
        in the output.
      skip: A `Tensor` of type `int32`.
        Positive scalar `Tensor` of dtype int32. The number of initial points of the
        Sobol sequence to skip.
      dtype: An optional `tf.DType` from: `tf.float32, tf.float64`. Defaults to `tf.float32`.
        The type of the sample. One of: `float32` or `float64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `dtype`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SobolSample", name,
          tld.op_callbacks, dim, num_results, skip, "dtype", dtype)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sobol_sample_eager_fallback(
            dim, num_results, skip, dtype=dtype, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if dtype is None:
      dtype = _dtypes.float32
    dtype = _execute.make_type(dtype, "dtype")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SobolSample", dim=dim, num_results=num_results, skip=skip,
                         dtype=dtype, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("dtype", _op._get_attr_type("dtype"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SobolSample", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SobolSample = tf_export("raw_ops.SobolSample")(_ops.to_raw_op(sobol_sample))
  
  
  def sobol_sample_eager_fallback(dim, num_results, skip, dtype, name, ctx):
    if dtype is None:
      dtype = _dtypes.float32
    dtype = _execute.make_type(dtype, "dtype")
    dim = _ops.convert_to_tensor(dim, _dtypes.int32)
    num_results = _ops.convert_to_tensor(num_results, _dtypes.int32)
    skip = _ops.convert_to_tensor(skip, _dtypes.int32)
    _inputs_flat = [dim, num_results, skip]
    _attrs = ("dtype", dtype)
    _result = _execute.execute(b"SobolSample", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SobolSample", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_bincount(indices, values, dense_shape, size, weights, binary_output=False, name=None):
    r"""Counts the number of occurrences of each value in an integer array.
  
    Outputs a vector with length `size` and the same dtype as `weights`. If
    `weights` are empty, then index `i` stores the number of times the value `i` is
    counted in `arr`. If `weights` are non-empty, then index `i` stores the sum of
    the value in `weights` at each index where the corresponding value in `arr` is
    `i`.
  
    Values in `arr` outside of the range [0, size) are ignored.
  
    Args:
      indices: A `Tensor` of type `int64`. 2D int64 `Tensor`.
      values: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        1D int `Tensor`.
      dense_shape: A `Tensor` of type `int64`. 1D int64 `Tensor`.
      size: A `Tensor`. Must have the same type as `values`.
        non-negative int scalar `Tensor`.
      weights: A `Tensor`. Must be one of the following types: `int32`, `int64`, `float32`, `float64`.
        is an int32, int64, float32, or float64 `Tensor` with the same
        shape as `input`, or a length-0 `Tensor`, in which case it acts as all weights
        equal to 1.
      binary_output: An optional `bool`. Defaults to `False`.
        bool; Whether the kernel should count the appearance or number of occurrences.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `weights`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SparseBincount", name,
          tld.op_callbacks, indices, values, dense_shape, size, weights,
          "binary_output", binary_output)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_bincount_eager_fallback(
            indices, values, dense_shape, size, weights,
            binary_output=binary_output, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if binary_output is None:
      binary_output = False
    binary_output = _execute.make_bool(binary_output, "binary_output")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseBincount", indices=indices, values=values,
                            dense_shape=dense_shape, size=size, weights=weights,
                            binary_output=binary_output, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("Tidx", _op._get_attr_type("Tidx"), "T",
                _op._get_attr_type("T"), "binary_output",
                _op._get_attr_bool("binary_output"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseBincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseBincount = tf_export("raw_ops.SparseBincount")(_ops.to_raw_op(sparse_bincount))
  
  
  def sparse_bincount_eager_fallback(indices, values, dense_shape, size, weights, binary_output, name, ctx):
    if binary_output is None:
      binary_output = False
    binary_output = _execute.make_bool(binary_output, "binary_output")
    _attr_Tidx, _inputs_Tidx = _execute.args_to_matching_eager([values, size], ctx)
    (values, size) = _inputs_Tidx
    _attr_T, (weights,) = _execute.args_to_matching_eager([weights], ctx)
    indices = _ops.convert_to_tensor(indices, _dtypes.int64)
    dense_shape = _ops.convert_to_tensor(dense_shape, _dtypes.int64)
    _inputs_flat = [indices, values, dense_shape, size, weights]
    _attrs = ("Tidx", _attr_Tidx, "T", _attr_T, "binary_output", binary_output)
    _result = _execute.execute(b"SparseBincount", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseBincount", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_mat_mul(a, b, transpose_a=False, transpose_b=False, a_is_sparse=False, b_is_sparse=False, name=None):
    r"""Multiply matrix "a" by matrix "b".
  
    The inputs must be two-dimensional matrices and the inner dimension of "a" must
    match the outer dimension of "b". Both "a" and "b" must be `Tensor`s not
    `SparseTensor`s.  This op is optimized for the case where at least one of "a" or
    "b" is sparse, in the sense that they have a large proportion of zero values.
    The breakeven for using this versus a dense matrix multiply on one platform was
    30% zero values in the sparse matrix.
  
    The gradient computation of this operation will only take advantage of sparsity
    in the input gradient when that gradient comes from a Relu.
  
    Args:
      a: A `Tensor`. Must be one of the following types: `float32`, `bfloat16`.
      b: A `Tensor`. Must be one of the following types: `float32`, `bfloat16`.
      transpose_a: An optional `bool`. Defaults to `False`.
      transpose_b: An optional `bool`. Defaults to `False`.
      a_is_sparse: An optional `bool`. Defaults to `False`.
      b_is_sparse: An optional `bool`. Defaults to `False`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `float32`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SparseMatMul", name,
          tld.op_callbacks, a, b, "transpose_a", transpose_a, "transpose_b",
          transpose_b, "a_is_sparse", a_is_sparse, "b_is_sparse", b_is_sparse)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_mat_mul_eager_fallback(
            a, b, transpose_a=transpose_a, transpose_b=transpose_b,
            a_is_sparse=a_is_sparse, b_is_sparse=b_is_sparse, name=name,
            ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if transpose_a is None:
      transpose_a = False
    transpose_a = _execute.make_bool(transpose_a, "transpose_a")
    if transpose_b is None:
      transpose_b = False
    transpose_b = _execute.make_bool(transpose_b, "transpose_b")
    if a_is_sparse is None:
      a_is_sparse = False
    a_is_sparse = _execute.make_bool(a_is_sparse, "a_is_sparse")
    if b_is_sparse is None:
      b_is_sparse = False
    b_is_sparse = _execute.make_bool(b_is_sparse, "b_is_sparse")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseMatMul", a=a, b=b, transpose_a=transpose_a,
                          transpose_b=transpose_b, a_is_sparse=a_is_sparse,
                          b_is_sparse=b_is_sparse, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("transpose_a", _op._get_attr_bool("transpose_a"), "transpose_b",
                _op._get_attr_bool("transpose_b"), "a_is_sparse",
                _op._get_attr_bool("a_is_sparse"), "b_is_sparse",
                _op._get_attr_bool("b_is_sparse"), "Ta",
                _op._get_attr_type("Ta"), "Tb", _op._get_attr_type("Tb"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseMatMul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseMatMul = tf_export("raw_ops.SparseMatMul")(_ops.to_raw_op(sparse_mat_mul))
  
  
  def sparse_mat_mul_eager_fallback(a, b, transpose_a, transpose_b, a_is_sparse, b_is_sparse, name, ctx):
    if transpose_a is None:
      transpose_a = False
    transpose_a = _execute.make_bool(transpose_a, "transpose_a")
    if transpose_b is None:
      transpose_b = False
    transpose_b = _execute.make_bool(transpose_b, "transpose_b")
    if a_is_sparse is None:
      a_is_sparse = False
    a_is_sparse = _execute.make_bool(a_is_sparse, "a_is_sparse")
    if b_is_sparse is None:
      b_is_sparse = False
    b_is_sparse = _execute.make_bool(b_is_sparse, "b_is_sparse")
    _attr_Ta, (a,) = _execute.args_to_matching_eager([a], ctx, _dtypes.float32)
    _attr_Tb, (b,) = _execute.args_to_matching_eager([b], ctx, _dtypes.float32)
    _inputs_flat = [a, b]
    _attrs = ("transpose_a", transpose_a, "transpose_b", transpose_b,
    "a_is_sparse", a_is_sparse, "b_is_sparse", b_is_sparse, "Ta", _attr_Ta,
    "Tb", _attr_Tb)
    _result = _execute.execute(b"SparseMatMul", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseMatMul", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_mean(data, indices, segment_ids, name=None):
    r"""Computes the mean along sparse segments of a tensor.
  
    See `tf.sparse.segment_sum` for usage examples.
  
    Like `SegmentMean`, but `segment_ids` can have rank less than `data`'s first
    dimension, selecting a subset of dimension 0, specified by `indices`.
  
    Args:
      data: A `Tensor`. Must be one of the following types: `bfloat16`, `float32`, `float64`.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Has same rank as `segment_ids`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SparseSegmentMean", name,
          tld.op_callbacks, data, indices, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_mean_eager_fallback(
            data, indices, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentMean", data=data, indices=indices,
                               segment_ids=segment_ids, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentMean", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentMean = tf_export("raw_ops.SparseSegmentMean")(_ops.to_raw_op(sparse_segment_mean))
  
  
  def sparse_segment_mean_eager_fallback(data, indices, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    _inputs_flat = [data, indices, segment_ids]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tsegmentids",
    _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentMean", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentMean", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_mean_grad(grad, indices, segment_ids, output_dim0, name=None):
    r"""Computes gradients for SparseSegmentMean.
  
    Returns tensor "output" with same shape as grad, except for dimension 0 whose
    value is output_dim0.
  
    Args:
      grad: A `Tensor`. Must be one of the following types: `float32`, `float64`.
        gradient propagated to the SparseSegmentMean op.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        indices passed to the corresponding SparseSegmentMean op.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        segment_ids passed to the corresponding SparseSegmentMean op.
      output_dim0: A `Tensor` of type `int32`.
        dimension 0 of "data" passed to SparseSegmentMean op.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `grad`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SparseSegmentMeanGrad", name,
          tld.op_callbacks, grad, indices, segment_ids, output_dim0)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_mean_grad_eager_fallback(
            grad, indices, segment_ids, output_dim0, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentMeanGrad", grad=grad, indices=indices,
                                   segment_ids=segment_ids,
                                   output_dim0=output_dim0, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentMeanGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentMeanGrad = tf_export("raw_ops.SparseSegmentMeanGrad")(_ops.to_raw_op(sparse_segment_mean_grad))
  
  
  def sparse_segment_mean_grad_eager_fallback(grad, indices, segment_ids, output_dim0, name, ctx):
    _attr_T, (grad,) = _execute.args_to_matching_eager([grad], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    output_dim0 = _ops.convert_to_tensor(output_dim0, _dtypes.int32)
    _inputs_flat = [grad, indices, segment_ids, output_dim0]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tsegmentids",
    _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentMeanGrad", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentMeanGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_mean_with_num_segments(data, indices, segment_ids, num_segments, name=None):
    r"""Computes the mean along sparse segments of a tensor.
  
    Like `SparseSegmentMean`, but allows missing ids in `segment_ids`. If an id is
    missing, the `output` tensor at that position will be zeroed.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Args:
      data: A `Tensor`. Must be one of the following types: `bfloat16`, `float32`, `float64`.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Has same rank as `segment_ids`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Values should be sorted and can be repeated.
      num_segments: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        Should equal the number of distinct segment IDs.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name,
          "SparseSegmentMeanWithNumSegments", name, tld.op_callbacks, data,
          indices, segment_ids, num_segments)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_mean_with_num_segments_eager_fallback(
            data, indices, segment_ids, num_segments, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentMeanWithNumSegments", data=data, indices=indices,
                                              segment_ids=segment_ids,
                                              num_segments=num_segments,
                                              name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tnumsegments",
                _op._get_attr_type("Tnumsegments"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentMeanWithNumSegments", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentMeanWithNumSegments = tf_export("raw_ops.SparseSegmentMeanWithNumSegments")(_ops.to_raw_op(sparse_segment_mean_with_num_segments))
  
  
  def sparse_segment_mean_with_num_segments_eager_fallback(data, indices, segment_ids, num_segments, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tnumsegments, (num_segments,) = _execute.args_to_matching_eager([num_segments], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    _inputs_flat = [data, indices, segment_ids, num_segments]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tnumsegments",
    _attr_Tnumsegments, "Tsegmentids", _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentMeanWithNumSegments", 1,
                               inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
                               name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentMeanWithNumSegments", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_sqrt_n(data, indices, segment_ids, name=None):
    r"""Computes the sum along sparse segments of a tensor divided by the sqrt of N.
  
    N is the size of the segment being reduced.
  
    See `tf.sparse.segment_sum` for usage examples.
  
    Args:
      data: A `Tensor`. Must be one of the following types: `bfloat16`, `float32`, `float64`.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Has same rank as `segment_ids`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SparseSegmentSqrtN", name,
          tld.op_callbacks, data, indices, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_sqrt_n_eager_fallback(
            data, indices, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentSqrtN", data=data, indices=indices,
                                segment_ids=segment_ids, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentSqrtN", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentSqrtN = tf_export("raw_ops.SparseSegmentSqrtN")(_ops.to_raw_op(sparse_segment_sqrt_n))
  
  
  def sparse_segment_sqrt_n_eager_fallback(data, indices, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    _inputs_flat = [data, indices, segment_ids]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tsegmentids",
    _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentSqrtN", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentSqrtN", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_sqrt_n_grad(grad, indices, segment_ids, output_dim0, name=None):
    r"""Computes gradients for SparseSegmentSqrtN.
  
    Returns tensor "output" with same shape as grad, except for dimension 0 whose
    value is output_dim0.
  
    Args:
      grad: A `Tensor`. Must be one of the following types: `float32`, `float64`.
        gradient propagated to the SparseSegmentSqrtN op.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        indices passed to the corresponding SparseSegmentSqrtN op.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        segment_ids passed to the corresponding SparseSegmentSqrtN op.
      output_dim0: A `Tensor` of type `int32`.
        dimension 0 of "data" passed to SparseSegmentSqrtN op.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `grad`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SparseSegmentSqrtNGrad", name,
          tld.op_callbacks, grad, indices, segment_ids, output_dim0)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_sqrt_n_grad_eager_fallback(
            grad, indices, segment_ids, output_dim0, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentSqrtNGrad", grad=grad, indices=indices,
                                    segment_ids=segment_ids,
                                    output_dim0=output_dim0, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentSqrtNGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentSqrtNGrad = tf_export("raw_ops.SparseSegmentSqrtNGrad")(_ops.to_raw_op(sparse_segment_sqrt_n_grad))
  
  
  def sparse_segment_sqrt_n_grad_eager_fallback(grad, indices, segment_ids, output_dim0, name, ctx):
    _attr_T, (grad,) = _execute.args_to_matching_eager([grad], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    output_dim0 = _ops.convert_to_tensor(output_dim0, _dtypes.int32)
    _inputs_flat = [grad, indices, segment_ids, output_dim0]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tsegmentids",
    _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentSqrtNGrad", 1,
                               inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
                               name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentSqrtNGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_sqrt_n_with_num_segments(data, indices, segment_ids, num_segments, name=None):
    r"""Computes the sum along sparse segments of a tensor divided by the sqrt of N.
  
    N is the size of the segment being reduced.
  
    Like `SparseSegmentSqrtN`, but allows missing ids in `segment_ids`. If an id is
    missing, the `output` tensor at that position will be zeroed.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Args:
      data: A `Tensor`. Must be one of the following types: `bfloat16`, `float32`, `float64`.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Has same rank as `segment_ids`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Values should be sorted and can be repeated.
      num_segments: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        Should equal the number of distinct segment IDs.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name,
          "SparseSegmentSqrtNWithNumSegments", name, tld.op_callbacks, data,
          indices, segment_ids, num_segments)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_sqrt_n_with_num_segments_eager_fallback(
            data, indices, segment_ids, num_segments, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentSqrtNWithNumSegments", data=data, indices=indices,
                                               segment_ids=segment_ids,
                                               num_segments=num_segments,
                                               name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tnumsegments",
                _op._get_attr_type("Tnumsegments"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentSqrtNWithNumSegments", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentSqrtNWithNumSegments = tf_export("raw_ops.SparseSegmentSqrtNWithNumSegments")(_ops.to_raw_op(sparse_segment_sqrt_n_with_num_segments))
  
  
  def sparse_segment_sqrt_n_with_num_segments_eager_fallback(data, indices, segment_ids, num_segments, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tnumsegments, (num_segments,) = _execute.args_to_matching_eager([num_segments], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    _inputs_flat = [data, indices, segment_ids, num_segments]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tnumsegments",
    _attr_Tnumsegments, "Tsegmentids", _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentSqrtNWithNumSegments", 1,
                               inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
                               name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentSqrtNWithNumSegments", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_sum(data, indices, segment_ids, name=None):
    r"""Computes the sum along sparse segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Like `SegmentSum`, but `segment_ids` can have rank less than `data`'s first
    dimension, selecting a subset of dimension 0, specified by `indices`.
  
    For example:
  
    ```python
    c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
  
    # Select two rows, one segment.
    tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 0]))
    # => [[0 0 0 0]]
  
    # Select two rows, two segment.
    tf.sparse_segment_sum(c, tf.constant([0, 1]), tf.constant([0, 1]))
    # => [[ 1  2  3  4]
    #     [-1 -2 -3 -4]]
  
    # Select all rows, two segments.
    tf.sparse_segment_sum(c, tf.constant([0, 1, 2]), tf.constant([0, 0, 1]))
    # => [[0 0 0 0]
    #     [5 6 7 8]]
  
    # Which is equivalent to:
    tf.segment_sum(c, tf.constant([0, 0, 1]))
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Has same rank as `segment_ids`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Values should be sorted and can be repeated.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SparseSegmentSum", name,
          tld.op_callbacks, data, indices, segment_ids)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_sum_eager_fallback(
            data, indices, segment_ids, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentSum", data=data, indices=indices,
                              segment_ids=segment_ids, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentSum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentSum = tf_export("raw_ops.SparseSegmentSum")(_ops.to_raw_op(sparse_segment_sum))
  
  
  def sparse_segment_sum_eager_fallback(data, indices, segment_ids, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    _inputs_flat = [data, indices, segment_ids]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tsegmentids",
    _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentSum", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentSum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sparse_segment_sum_with_num_segments(data, indices, segment_ids, num_segments, name=None):
    r"""Computes the sum along sparse segments of a tensor.
  
    Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is
    missing, the `output` tensor at that position will be zeroed.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/sparse#Segmentation)
    for an explanation of segments.
  
    For example:
  
    ```python
    c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])
  
    tf.sparse_segment_sum_with_num_segments(
        c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3)
    # => [[0 0 0 0]
    #     [0 0 0 0]
    #     [0 0 0 0]]
  
    tf.sparse_segment_sum_with_num_segments(c,
                                            tf.constant([0, 1]),
                                            tf.constant([0, 2],
                                            num_segments=4))
    # => [[ 1  2  3  4]
    #     [ 0  0  0  0]
    #     [-1 -2 -3 -4]
    #     [ 0  0  0  0]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      indices: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Has same rank as `segment_ids`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A 1-D tensor. Values should be sorted and can be repeated.
      num_segments: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        Should equal the number of distinct segment IDs.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name,
          "SparseSegmentSumWithNumSegments", name, tld.op_callbacks, data,
          indices, segment_ids, num_segments)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sparse_segment_sum_with_num_segments_eager_fallback(
            data, indices, segment_ids, num_segments, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SparseSegmentSumWithNumSegments", data=data, indices=indices,
                                             segment_ids=segment_ids,
                                             num_segments=num_segments,
                                             name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tidx",
                _op._get_attr_type("Tidx"), "Tnumsegments",
                _op._get_attr_type("Tnumsegments"), "Tsegmentids",
                _op._get_attr_type("Tsegmentids"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SparseSegmentSumWithNumSegments", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SparseSegmentSumWithNumSegments = tf_export("raw_ops.SparseSegmentSumWithNumSegments")(_ops.to_raw_op(sparse_segment_sum_with_num_segments))
  
  
  def sparse_segment_sum_with_num_segments_eager_fallback(data, indices, segment_ids, num_segments, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tidx, (indices,) = _execute.args_to_matching_eager([indices], ctx, _dtypes.int32)
    _attr_Tnumsegments, (num_segments,) = _execute.args_to_matching_eager([num_segments], ctx, _dtypes.int32)
    _attr_Tsegmentids, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx, _dtypes.int32)
    _inputs_flat = [data, indices, segment_ids, num_segments]
    _attrs = ("T", _attr_T, "Tidx", _attr_Tidx, "Tnumsegments",
    _attr_Tnumsegments, "Tsegmentids", _attr_Tsegmentids)
    _result = _execute.execute(b"SparseSegmentSumWithNumSegments", 1,
                               inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
                               name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SparseSegmentSumWithNumSegments", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sqrt(x, name=None):
    r"""Computes square root of x element-wise.
  
    I.e., \\(y = \sqrt{x} = x^{1/2}\\).
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Sqrt", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sqrt_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Sqrt", x=x, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Sqrt", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Sqrt = tf_export("raw_ops.Sqrt")(_ops.to_raw_op(sqrt))
  
  
  def sqrt_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Sqrt", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Sqrt", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sqrt_grad(y, dy, name=None):
    r"""Computes the gradient for the sqrt of `x` wrt its input.
  
    Specifically, `grad = dy * 0.5 / y`, where `y = sqrt(x)`, and `dy`
    is the corresponding input gradient.
  
    Args:
      y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      dy: A `Tensor`. Must have the same type as `y`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `y`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SqrtGrad", name,
          tld.op_callbacks, y, dy)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sqrt_grad_eager_fallback(
            y, dy, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SqrtGrad", y=y, dy=dy, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SqrtGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SqrtGrad = tf_export("raw_ops.SqrtGrad")(_ops.to_raw_op(sqrt_grad))
  
  
  def sqrt_grad_eager_fallback(y, dy, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([y, dy], ctx)
    (y, dy) = _inputs_T
    _inputs_flat = [y, dy]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"SqrtGrad", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SqrtGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.square', 'square')
  def square(x, name=None):
    r"""Computes square of x element-wise.
  
    I.e., \\(y = x * x = x^2\\).
  
    >>> tf.math.square([-2., 0., 3.])
    <tf.Tensor: shape=(3,), dtype=float32, numpy=array([4., 0., 9.], dtype=float32)>
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Square", name,
          tld.op_callbacks, x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return square_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              square, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Square", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            square, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Square", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Square = tf_export("raw_ops.Square")(_ops.to_raw_op(square))
  
  
  def square_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Square", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Square", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.squared_difference', v1=['math.squared_difference', 'squared_difference'])
  @deprecated_endpoints('squared_difference')
  def squared_difference(x, y, name=None):
    r"""Returns (x - y)(x - y) element-wise.
  
    *NOTE*: `math.squared_difference` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "SquaredDifference", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return squared_difference_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              squared_difference, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "SquaredDifference", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            squared_difference, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "SquaredDifference", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  SquaredDifference = tf_export("raw_ops.SquaredDifference")(_ops.to_raw_op(squared_difference))
  
  
  def squared_difference_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"SquaredDifference", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "SquaredDifference", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def sub(x, y, name=None):
    r"""Returns x - y element-wise.
  
    *NOTE*: `Subtract` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`, `uint32`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Sub", name, tld.op_callbacks,
          x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return sub_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Sub", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Sub", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Sub = tf_export("raw_ops.Sub")(_ops.to_raw_op(sub))
  
  
  def sub_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Sub", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Sub", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def _sum(input, axis, keep_dims=False, name=None):
    r"""Computes the sum of elements across dimensions of a tensor.
  
    Reduces `input` along the dimensions given in `axis`. Unless
    `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
    `axis`. If `keep_dims` is true, the reduced dimensions are
    retained with length 1.
  
    Args:
      input: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
        The tensor to reduce.
      axis: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        The dimensions to reduce. Must be in the range
        `[-rank(input), rank(input))`.
      keep_dims: An optional `bool`. Defaults to `False`.
        If true, retain reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Sum", name, tld.op_callbacks,
          input, axis, "keep_dims", keep_dims)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return _sum_eager_fallback(
            input, axis, keep_dims=keep_dims, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Sum", input=input, reduction_indices=axis, keep_dims=keep_dims,
                 name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("keep_dims", _op._get_attr_bool("keep_dims"), "T",
                _op._get_attr_type("T"), "Tidx", _op._get_attr_type("Tidx"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Sum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Sum = tf_export("raw_ops.Sum")(_ops.to_raw_op(_sum))
  
  
  def _sum_eager_fallback(input, axis, keep_dims, name, ctx):
    if keep_dims is None:
      keep_dims = False
    keep_dims = _execute.make_bool(keep_dims, "keep_dims")
    _attr_T, (input,) = _execute.args_to_matching_eager([input], ctx)
    _attr_Tidx, (axis,) = _execute.args_to_matching_eager([axis], ctx, _dtypes.int32)
    _inputs_flat = [input, axis]
    _attrs = ("keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx)
    _result = _execute.execute(b"Sum", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Sum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.tan', 'tan')
  def tan(x, name=None):
    r"""Computes tan of x element-wise.
  
      Given an input tensor, this function computes tangent of every
      element in the tensor. Input range is `(-inf, inf)` and
      output range is `(-inf, inf)`. If input lies outside the boundary, `nan`
      is returned.
  
      ```python
      x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])
      tf.math.tan(x) ==> [nan 0.45231566 -0.5463025 1.5574077 2.572152 -1.7925274 0.32097113 nan]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int8`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Tan", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return tan_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              tan, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Tan", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            tan, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Tan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Tan = tf_export("raw_ops.Tan")(_ops.to_raw_op(tan))
  
  
  def tan_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Tan", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Tan", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.tanh', 'nn.tanh', 'tanh')
  def tanh(x, name=None):
    r"""Computes hyperbolic tangent of `x` element-wise.
  
      Given an input tensor, this function computes hyperbolic tangent of every
      element in the tensor. Input range is `[-inf, inf]` and
      output range is `[-1,1]`.
  
      ```python
      x = tf.constant([-float("inf"), -5, -0.5, 1, 1.2, 2, 3, float("inf")])
      tf.math.tanh(x) ==> [-1. -0.99990916 -0.46211717 0.7615942 0.8336547 0.9640276 0.9950547 1.]
      ```
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Tanh", name, tld.op_callbacks,
          x)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return tanh_eager_fallback(
            x, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              tanh, (), dict(x=x, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Tanh", x=x, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            tanh, (), dict(x=x, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Tanh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Tanh = tf_export("raw_ops.Tanh")(_ops.to_raw_op(tanh))
  
  
  def tanh_eager_fallback(x, name, ctx):
    _attr_T, (x,) = _execute.args_to_matching_eager([x], ctx)
    _inputs_flat = [x]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Tanh", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Tanh", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def tanh_grad(y, dy, name=None):
    r"""Computes the gradient for the tanh of `x` wrt its input.
  
    Specifically, `grad = dy * (1 - y*y)`, where `y = tanh(x)`, and `dy`
    is the corresponding input gradient.
  
    Args:
      y: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `complex64`, `complex128`.
      dy: A `Tensor`. Must have the same type as `y`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `y`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "TanhGrad", name,
          tld.op_callbacks, y, dy)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return tanh_grad_eager_fallback(
            y, dy, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "TanhGrad", y=y, dy=dy, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "TanhGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  TanhGrad = tf_export("raw_ops.TanhGrad")(_ops.to_raw_op(tanh_grad))
  
  
  def tanh_grad_eager_fallback(y, dy, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([y, dy], ctx)
    (y, dy) = _inputs_T
    _inputs_flat = [y, dy]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"TanhGrad", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "TanhGrad", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('truncatediv')
  def truncate_div(x, y, name=None):
    r"""Returns x / y element-wise for integer types.
  
    Truncation designates that negative numbers will round fractional quantities
    toward zero. I.e. -7 / 5 = -1. This matches C semantics but it is different
    than Python semantics. See `FloorDiv` for a division function that matches
    Python Semantics.
  
    *NOTE*: `truncatediv` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `uint8`, `int8`, `uint16`, `int16`, `int32`, `int64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "TruncateDiv", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return truncate_div_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              truncate_div, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "TruncateDiv", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            truncate_div, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "TruncateDiv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  TruncateDiv = tf_export("raw_ops.TruncateDiv")(_ops.to_raw_op(truncate_div))
  
  
  def truncate_div_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"TruncateDiv", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "TruncateDiv", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('truncatemod')
  def truncate_mod(x, y, name=None):
    r"""Returns element-wise remainder of division. This emulates C semantics in that
  
    the result here is consistent with a truncating divide. E.g. `truncate(x / y) *
    y + truncate_mod(x, y) = x`.
  
    *NOTE*: `truncatemod` supports broadcasting. More about broadcasting
    [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `int32`, `int64`, `bfloat16`, `half`, `float32`, `float64`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "TruncateMod", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return truncate_mod_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              truncate_mod, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "TruncateMod", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            truncate_mod, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "TruncateMod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  TruncateMod = tf_export("raw_ops.TruncateMod")(_ops.to_raw_op(truncate_mod))
  
  
  def truncate_mod_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"TruncateMod", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "TruncateMod", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.unsorted_segment_max', v1=['math.unsorted_segment_max', 'unsorted_segment_max'])
  @deprecated_endpoints('unsorted_segment_max')
  def unsorted_segment_max(data, segment_ids, num_segments, name=None):
    r"""Computes the maximum along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    This operator is similar to the unsorted segment sum operator found
    [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
    Instead of computing the sum over segments, it computes the maximum such that:
  
    \\(output_i = \max_{j...} data[j...]\\) where max is over tuples `j...` such
    that `segment_ids[j...] == i`.
  
    If the maximum is empty for a given segment ID `i`, it outputs the smallest
    possible value for the specific numeric type,
    `output[i] = numeric_limits<T>::lowest()`.
  
    If the given segment ID `i` is negative, then the corresponding value is
    dropped, and will not be included in the result.
  
    <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
    <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentMax.png" alt>
    </div>
  
    For example:
  
    ``` python
    c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
    tf.unsorted_segment_max(c, tf.constant([0, 1, 0]), num_segments=2)
    # ==> [[ 4,  3, 3, 4],
    #       [5,  6, 7, 8]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A tensor whose shape is a prefix of `data.shape`.
      num_segments: A `Tensor`. Must be one of the following types: `int32`, `int64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "UnsortedSegmentMax", name,
          tld.op_callbacks, data, segment_ids, num_segments)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return unsorted_segment_max_eager_fallback(
            data, segment_ids, num_segments, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              unsorted_segment_max, (), dict(data=data, segment_ids=segment_ids,
                                             num_segments=num_segments,
                                             name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "UnsortedSegmentMax", data=data, segment_ids=segment_ids,
                                num_segments=num_segments, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            unsorted_segment_max, (), dict(data=data, segment_ids=segment_ids,
                                           num_segments=num_segments, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"), "Tnumsegments",
                _op._get_attr_type("Tnumsegments"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "UnsortedSegmentMax", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  UnsortedSegmentMax = tf_export("raw_ops.UnsortedSegmentMax")(_ops.to_raw_op(unsorted_segment_max))
  
  
  def unsorted_segment_max_eager_fallback(data, segment_ids, num_segments, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _attr_Tnumsegments, (num_segments,) = _execute.args_to_matching_eager([num_segments], ctx, _dtypes.int32)
    _inputs_flat = [data, segment_ids, num_segments]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "Tnumsegments",
    _attr_Tnumsegments)
    _result = _execute.execute(b"UnsortedSegmentMax", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "UnsortedSegmentMax", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.unsorted_segment_min', v1=['math.unsorted_segment_min', 'unsorted_segment_min'])
  @deprecated_endpoints('unsorted_segment_min')
  def unsorted_segment_min(data, segment_ids, num_segments, name=None):
    r"""Computes the minimum along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    This operator is similar to the unsorted segment sum operator found
    [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
    Instead of computing the sum over segments, it computes the minimum such that:
  
    \\(output_i = \min_{j...} data_[j...]\\) where min is over tuples `j...` such
    that `segment_ids[j...] == i`.
  
    If the minimum is empty for a given segment ID `i`, it outputs the largest
    possible value for the specific numeric type,
    `output[i] = numeric_limits<T>::max()`.
  
    For example:
  
    ``` python
    c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
    tf.unsorted_segment_min(c, tf.constant([0, 1, 0]), num_segments=2)
    # ==> [[ 1,  2, 2, 1],
    #       [5,  6, 7, 8]]
    ```
  
    If the given segment ID `i` is negative, then the corresponding value is
    dropped, and will not be included in the result.
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A tensor whose shape is a prefix of `data.shape`.
      num_segments: A `Tensor`. Must be one of the following types: `int32`, `int64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "UnsortedSegmentMin", name,
          tld.op_callbacks, data, segment_ids, num_segments)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return unsorted_segment_min_eager_fallback(
            data, segment_ids, num_segments, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              unsorted_segment_min, (), dict(data=data, segment_ids=segment_ids,
                                             num_segments=num_segments,
                                             name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "UnsortedSegmentMin", data=data, segment_ids=segment_ids,
                                num_segments=num_segments, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            unsorted_segment_min, (), dict(data=data, segment_ids=segment_ids,
                                           num_segments=num_segments, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"), "Tnumsegments",
                _op._get_attr_type("Tnumsegments"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "UnsortedSegmentMin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  UnsortedSegmentMin = tf_export("raw_ops.UnsortedSegmentMin")(_ops.to_raw_op(unsorted_segment_min))
  
  
  def unsorted_segment_min_eager_fallback(data, segment_ids, num_segments, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _attr_Tnumsegments, (num_segments,) = _execute.args_to_matching_eager([num_segments], ctx, _dtypes.int32)
    _inputs_flat = [data, segment_ids, num_segments]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "Tnumsegments",
    _attr_Tnumsegments)
    _result = _execute.execute(b"UnsortedSegmentMin", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "UnsortedSegmentMin", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.unsorted_segment_prod', v1=['math.unsorted_segment_prod', 'unsorted_segment_prod'])
  @deprecated_endpoints('unsorted_segment_prod')
  def unsorted_segment_prod(data, segment_ids, num_segments, name=None):
    r"""Computes the product along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    This operator is similar to the unsorted segment sum operator found
    [(here)](../../../api_docs/python/math_ops.md#UnsortedSegmentSum).
    Instead of computing the sum over segments, it computes the product of all
    entries belonging to a segment such that:
  
    \\(output_i = \prod_{j...} data[j...]\\) where the product is over tuples
    `j...` such that `segment_ids[j...] == i`.
  
    For example:
  
    ``` python
    c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
    tf.unsorted_segment_prod(c, tf.constant([0, 1, 0]), num_segments=2)
    # ==> [[ 4,  6, 6, 4],
    #       [5,  6, 7, 8]]
    ```
  
    If there is no entry for a given segment ID `i`, it outputs 1.
  
    If the given segment ID `i` is negative, then the corresponding value is
    dropped, and will not be included in the result.
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A tensor whose shape is a prefix of `data.shape`.
      num_segments: A `Tensor`. Must be one of the following types: `int32`, `int64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "UnsortedSegmentProd", name,
          tld.op_callbacks, data, segment_ids, num_segments)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return unsorted_segment_prod_eager_fallback(
            data, segment_ids, num_segments, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              unsorted_segment_prod, (), dict(data=data,
                                              segment_ids=segment_ids,
                                              num_segments=num_segments,
                                              name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "UnsortedSegmentProd", data=data, segment_ids=segment_ids,
                                 num_segments=num_segments, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            unsorted_segment_prod, (), dict(data=data, segment_ids=segment_ids,
                                            num_segments=num_segments,
                                            name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"), "Tnumsegments",
                _op._get_attr_type("Tnumsegments"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "UnsortedSegmentProd", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  UnsortedSegmentProd = tf_export("raw_ops.UnsortedSegmentProd")(_ops.to_raw_op(unsorted_segment_prod))
  
  
  def unsorted_segment_prod_eager_fallback(data, segment_ids, num_segments, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _attr_Tnumsegments, (num_segments,) = _execute.args_to_matching_eager([num_segments], ctx, _dtypes.int32)
    _inputs_flat = [data, segment_ids, num_segments]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "Tnumsegments",
    _attr_Tnumsegments)
    _result = _execute.execute(b"UnsortedSegmentProd", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "UnsortedSegmentProd", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.unsorted_segment_sum', v1=['math.unsorted_segment_sum', 'unsorted_segment_sum'])
  @deprecated_endpoints('unsorted_segment_sum')
  def unsorted_segment_sum(data, segment_ids, num_segments, name=None):
    r"""Computes the sum along segments of a tensor.
  
    Read
    [the section on segmentation](https://tensorflow.org/api_docs/python/tf/math#Segmentation)
    for an explanation of segments.
  
    Computes a tensor such that
    \\(output[i] = \sum_{j...} data[j...]\\) where the sum is over tuples `j...` such
    that `segment_ids[j...] == i`.  Unlike `SegmentSum`, `segment_ids`
    need not be sorted and need not cover all values in the full
    range of valid values.
  
    If the sum is empty for a given segment ID `i`, `output[i] = 0`.
    If the given segment ID `i` is negative, the value is dropped and will not be
    added to the sum of the segment.
  
    `num_segments` should equal the number of distinct segment IDs.
  
    <div style="width:70%; margin:auto; margin-bottom:10px; margin-top:20px;">
    <img style="width:100%" src="https://www.tensorflow.org/images/UnsortedSegmentSum.png" alt>
    </div>
  
    ``` python
    c = tf.constant([[1,2,3,4], [5,6,7,8], [4,3,2,1]])
    tf.unsorted_segment_sum(c, tf.constant([0, 1, 0]), num_segments=2)
    # ==> [[ 5,  5, 5, 5],
    #       [5,  6, 7, 8]]
    ```
  
    Args:
      data: A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `complex64`, `int64`, `qint8`, `quint8`, `qint32`, `bfloat16`, `uint16`, `complex128`, `half`, `uint32`, `uint64`.
      segment_ids: A `Tensor`. Must be one of the following types: `int32`, `int64`.
        A tensor whose shape is a prefix of `data.shape`.
      num_segments: A `Tensor`. Must be one of the following types: `int32`, `int64`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `data`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "UnsortedSegmentSum", name,
          tld.op_callbacks, data, segment_ids, num_segments)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return unsorted_segment_sum_eager_fallback(
            data, segment_ids, num_segments, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              unsorted_segment_sum, (), dict(data=data, segment_ids=segment_ids,
                                             num_segments=num_segments,
                                             name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "UnsortedSegmentSum", data=data, segment_ids=segment_ids,
                                num_segments=num_segments, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            unsorted_segment_sum, (), dict(data=data, segment_ids=segment_ids,
                                           num_segments=num_segments, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"), "Tindices",
                _op._get_attr_type("Tindices"), "Tnumsegments",
                _op._get_attr_type("Tnumsegments"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "UnsortedSegmentSum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  UnsortedSegmentSum = tf_export("raw_ops.UnsortedSegmentSum")(_ops.to_raw_op(unsorted_segment_sum))
  
  
  def unsorted_segment_sum_eager_fallback(data, segment_ids, num_segments, name, ctx):
    _attr_T, (data,) = _execute.args_to_matching_eager([data], ctx)
    _attr_Tindices, (segment_ids,) = _execute.args_to_matching_eager([segment_ids], ctx)
    _attr_Tnumsegments, (num_segments,) = _execute.args_to_matching_eager([num_segments], ctx, _dtypes.int32)
    _inputs_flat = [data, segment_ids, num_segments]
    _attrs = ("T", _attr_T, "Tindices", _attr_Tindices, "Tnumsegments",
    _attr_Tnumsegments)
    _result = _execute.execute(b"UnsortedSegmentSum", 1, inputs=_inputs_flat,
                               attrs=_attrs, ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "UnsortedSegmentSum", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.xdivy')
  def xdivy(x, y, name=None):
    r"""Returns 0 if x == 0, and x / y otherwise, elementwise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Xdivy", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return xdivy_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              xdivy, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Xdivy", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            xdivy, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Xdivy", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Xdivy = tf_export("raw_ops.Xdivy")(_ops.to_raw_op(xdivy))
  
  
  def xdivy_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Xdivy", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Xdivy", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  def xlog1py(x, y, name=None):
    r"""Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Xlog1py", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return xlog1py_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
    # Add nodes to the TensorFlow graph.
    _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Xlog1py", x=x, y=y, name=name)
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Xlog1py", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Xlog1py = tf_export("raw_ops.Xlog1py")(_ops.to_raw_op(xlog1py))
  
  
  def xlog1py_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Xlog1py", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Xlog1py", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.xlogy')
  def xlogy(x, y, name=None):
    r"""Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
  
    Args:
      x: A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `complex64`, `complex128`.
      y: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Xlogy", name,
          tld.op_callbacks, x, y)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return xlogy_eager_fallback(
            x, y, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              xlogy, (), dict(x=x, y=y, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Xlogy", x=x, y=y, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            xlogy, (), dict(x=x, y=y, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Xlogy", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Xlogy = tf_export("raw_ops.Xlogy")(_ops.to_raw_op(xlogy))
  
  
  def xlogy_eager_fallback(x, y, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, y], ctx)
    (x, y) = _inputs_T
    _inputs_flat = [x, y]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Xlogy", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Xlogy", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  
  @_dispatch.add_dispatch_list
  @tf_export('math.zeta', v1=['math.zeta', 'zeta'])
  @deprecated_endpoints('zeta')
  def zeta(x, q, name=None):
    r"""Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
  
    The Hurwitz zeta function is defined as:
  
  
    \\(\zeta(x, q) = \sum_{n=0}^{\infty} (q + n)^{-x}\\)
  
    Args:
      x: A `Tensor`. Must be one of the following types: `float32`, `float64`.
      q: A `Tensor`. Must have the same type as `x`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `x`.
    """
    _ctx = _context._context or _context.context()
    tld = _ctx._thread_local_data
    if tld.is_eager:
      try:
        _result = pywrap_tfe.TFE_Py_FastPathExecute(
          _ctx._context_handle, tld.device_name, "Zeta", name, tld.op_callbacks,
          x, q)
        return _result
      except _core._NotOkStatusException as e:
        _ops.raise_from_not_ok_status(e, name)
      except _core._FallbackException:
        pass
      try:
        return zeta_eager_fallback(
            x, q, name=name, ctx=_ctx)
      except _core._SymbolicException:
        pass  # Add nodes to the TensorFlow graph.
      except (TypeError, ValueError):
        result = _dispatch.dispatch(
              zeta, (), dict(x=x, q=q, name=name)
            )
        if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
          return result
        raise
    # Add nodes to the TensorFlow graph.
    try:
      _, _, _op, _outputs = _op_def_library._apply_op_helper(
          "Zeta", x=x, q=q, name=name)
    except (TypeError, ValueError):
      result = _dispatch.dispatch(
            zeta, (), dict(x=x, q=q, name=name)
          )
      if result is not _dispatch.OpDispatcher.NOT_SUPPORTED:
        return result
      raise
    _result = _outputs[:]
    if _execute.must_record_gradient():
      _attrs = ("T", _op._get_attr_type("T"))
      _inputs_flat = _op.inputs
      _execute.record_gradient(
          "Zeta", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  Zeta = tf_export("raw_ops.Zeta")(_ops.to_raw_op(zeta))
  
  
  def zeta_eager_fallback(x, q, name, ctx):
    _attr_T, _inputs_T = _execute.args_to_matching_eager([x, q], ctx)
    (x, q) = _inputs_T
    _inputs_flat = [x, q]
    _attrs = ("T", _attr_T)
    _result = _execute.execute(b"Zeta", 1, inputs=_inputs_flat, attrs=_attrs,
                               ctx=ctx, name=name)
    if _execute.must_record_gradient():
      _execute.record_gradient(
          "Zeta", _inputs_flat, _attrs, _result)
    _result, = _result
    return _result
  
  