text: """Python wrappers around TensorFlow ops.
  
  This file is MACHINE GENERATED! Do not edit.
  """
  
  import collections as _collections
  import six as _six
  
  from tensorflow.python import pywrap_tensorflow as _pywrap_tensorflow
  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 errors as _errors
  from tensorflow.python.framework import tensor_shape as _tensor_shape
  
  from tensorflow.core.framework import op_def_pb2 as _op_def_pb2
  # Needed to trigger the call to _set_call_cpp_shape_fn.
  from tensorflow.python.framework import common_shapes as _common_shapes
  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.tf_export import tf_export
  
  
  def eager_py_func(input, token, Tout, name=None):
    r"""Eagerly executes a python function to compute func(input)->output. The
  
    semantics of the input, output, and attributes are the same as those for
  
    PyFunc.
  
    Args:
      input: A list of `Tensor` objects.
      token: A `string`.
      Tout: A list of `tf.DTypes`.
      name: A name for the operation (optional).
  
    Returns:
      A list of `Tensor` objects of type `Tout`.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      token = _execute.make_str(token, "token")
      if not isinstance(Tout, (list, tuple)):
        raise TypeError(
            "Expected list for 'Tout' argument to "
            "'eager_py_func' Op, not %r." % Tout)
      Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
      _, _, _op = _op_def_lib._apply_op_helper(
          "EagerPyFunc", input=input, token=token, Tout=Tout, name=name)
      _result = _op.outputs[:]
      if not _result:
        return _op
      _inputs_flat = _op.inputs
      _attrs = ("token", _op.get_attr("token"), "Tin", _op.get_attr("Tin"),
                "Tout", _op.get_attr("Tout"))
      _execute.record_gradient(
        "EagerPyFunc", _inputs_flat, _attrs, _result, name)
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name, "EagerPyFunc",
          name, _ctx._post_execution_callbacks, input, "token", token, "Tout",
          Tout)
        return _result
      except _core._FallbackException:
        return eager_py_func_eager_fallback(
            input, token=token, Tout=Tout, name=name, ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def eager_py_func_eager_fallback(input, token, Tout, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function eager_py_func
    """
    _ctx = ctx if ctx else _context.context()
    token = _execute.make_str(token, "token")
    if not isinstance(Tout, (list, tuple)):
      raise TypeError(
          "Expected list for 'Tout' argument to "
          "'eager_py_func' Op, not %r." % Tout)
    Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
    _attr_Tin, input = _execute.convert_to_mixed_eager_tensors(input, _ctx)
    _inputs_flat = list(input)
    _attrs = ("token", token, "Tin", _attr_Tin, "Tout", Tout)
    _result = _execute.execute(b"EagerPyFunc", len(Tout), inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "EagerPyFunc", _inputs_flat, _attrs, _result, name)
    return _result
  
  
  def py_func(input, token, Tout, name=None):
    r"""Invokes a python function to compute func(input)->output.
  
    This operation is considered stateful. For a stateless version, see
  
    PyFuncStateless.
  
    Args:
      input: A list of `Tensor` objects.
        List of Tensors that will provide input to the Op.
      token: A `string`.
        A token representing a registered python function in this address space.
      Tout: A list of `tf.DTypes`. Data types of the outputs from the op.
  
        The length of the list specifies the number of outputs.
      name: A name for the operation (optional).
  
    Returns:
      A list of `Tensor` objects of type `Tout`.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      token = _execute.make_str(token, "token")
      if not isinstance(Tout, (list, tuple)):
        raise TypeError(
            "Expected list for 'Tout' argument to "
            "'py_func' Op, not %r." % Tout)
      Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
      _, _, _op = _op_def_lib._apply_op_helper(
          "PyFunc", input=input, token=token, Tout=Tout, name=name)
      _result = _op.outputs[:]
      if not _result:
        return _op
      _inputs_flat = _op.inputs
      _attrs = ("token", _op.get_attr("token"), "Tin", _op.get_attr("Tin"),
                "Tout", _op.get_attr("Tout"))
      _execute.record_gradient(
        "PyFunc", _inputs_flat, _attrs, _result, name)
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name, "PyFunc", name,
          _ctx._post_execution_callbacks, input, "token", token, "Tout", Tout)
        return _result
      except _core._FallbackException:
        return py_func_eager_fallback(
            input, token=token, Tout=Tout, name=name, ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def py_func_eager_fallback(input, token, Tout, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function py_func
    """
    _ctx = ctx if ctx else _context.context()
    token = _execute.make_str(token, "token")
    if not isinstance(Tout, (list, tuple)):
      raise TypeError(
          "Expected list for 'Tout' argument to "
          "'py_func' Op, not %r." % Tout)
    Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
    _attr_Tin, input = _execute.convert_to_mixed_eager_tensors(input, _ctx)
    _inputs_flat = list(input)
    _attrs = ("token", token, "Tin", _attr_Tin, "Tout", Tout)
    _result = _execute.execute(b"PyFunc", len(Tout), inputs=_inputs_flat,
                               attrs=_attrs, ctx=_ctx, name=name)
    _execute.record_gradient(
        "PyFunc", _inputs_flat, _attrs, _result, name)
    return _result
  
  
  def py_func_stateless(input, token, Tout, name=None):
    r"""A stateless version of PyFunc.
  
    Args:
      input: A list of `Tensor` objects.
      token: A `string`.
      Tout: A list of `tf.DTypes`.
      name: A name for the operation (optional).
  
    Returns:
      A list of `Tensor` objects of type `Tout`.
    """
    _ctx = _context._context
    if _ctx is None or not _ctx._eager_context.is_eager:
      token = _execute.make_str(token, "token")
      if not isinstance(Tout, (list, tuple)):
        raise TypeError(
            "Expected list for 'Tout' argument to "
            "'py_func_stateless' Op, not %r." % Tout)
      Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
      _, _, _op = _op_def_lib._apply_op_helper(
          "PyFuncStateless", input=input, token=token, Tout=Tout, name=name)
      _result = _op.outputs[:]
      _inputs_flat = _op.inputs
      _attrs = ("token", _op.get_attr("token"), "Tin", _op.get_attr("Tin"),
                "Tout", _op.get_attr("Tout"))
      _execute.record_gradient(
        "PyFuncStateless", _inputs_flat, _attrs, _result, name)
      return _result
  
    else:
      try:
        _result = _pywrap_tensorflow.TFE_Py_FastPathExecute(
          _ctx._context_handle, _ctx._eager_context.device_name,
          "PyFuncStateless", name, _ctx._post_execution_callbacks, input,
          "token", token, "Tout", Tout)
        return _result
      except _core._FallbackException:
        return py_func_stateless_eager_fallback(
            input, token=token, Tout=Tout, name=name, ctx=_ctx)
      except _core._NotOkStatusException as e:
        if name is not None:
          message = e.message + " name: " + name
        else:
          message = e.message
        _six.raise_from(_core._status_to_exception(e.code, message), None)
  
  
  def py_func_stateless_eager_fallback(input, token, Tout, name=None, ctx=None):
    r"""This is the slowpath function for Eager mode.
    This is for function py_func_stateless
    """
    _ctx = ctx if ctx else _context.context()
    token = _execute.make_str(token, "token")
    if not isinstance(Tout, (list, tuple)):
      raise TypeError(
          "Expected list for 'Tout' argument to "
          "'py_func_stateless' Op, not %r." % Tout)
    Tout = [_execute.make_type(_t, "Tout") for _t in Tout]
    _attr_Tin, input = _execute.convert_to_mixed_eager_tensors(input, _ctx)
    _inputs_flat = list(input)
    _attrs = ("token", token, "Tin", _attr_Tin, "Tout", Tout)
    _result = _execute.execute(b"PyFuncStateless", len(Tout),
                               inputs=_inputs_flat, attrs=_attrs, ctx=_ctx,
                               name=name)
    _execute.record_gradient(
        "PyFuncStateless", _inputs_flat, _attrs, _result, name)
    return _result
  
  def _InitOpDefLibrary(op_list_proto_bytes):
    op_list = _op_def_pb2.OpList()
    op_list.ParseFromString(op_list_proto_bytes)
    _op_def_registry.register_op_list(op_list)
    op_def_lib = _op_def_library.OpDefLibrary()
    op_def_lib.add_op_list(op_list)
    return op_def_lib
  # op {
  #   name: "EagerPyFunc"
  #   input_arg {
  #     name: "input"
  #     type_list_attr: "Tin"
  #   }
  #   output_arg {
  #     name: "output"
  #     type_list_attr: "Tout"
  #   }
  #   attr {
  #     name: "token"
  #     type: "string"
  #   }
  #   attr {
  #     name: "Tin"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "Tout"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   is_stateful: true
  # }
  # op {
  #   name: "PyFunc"
  #   input_arg {
  #     name: "input"
  #     type_list_attr: "Tin"
  #   }
  #   output_arg {
  #     name: "output"
  #     type_list_attr: "Tout"
  #   }
  #   attr {
  #     name: "token"
  #     type: "string"
  #   }
  #   attr {
  #     name: "Tin"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "Tout"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   is_stateful: true
  # }
  # op {
  #   name: "PyFuncStateless"
  #   input_arg {
  #     name: "input"
  #     type_list_attr: "Tin"
  #   }
  #   output_arg {
  #     name: "output"
  #     type_list_attr: "Tout"
  #   }
  #   attr {
  #     name: "token"
  #     type: "string"
  #   }
  #   attr {
  #     name: "Tin"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  #   attr {
  #     name: "Tout"
  #     type: "list(type)"
  #     has_minimum: true
  #   }
  # }
  _op_def_lib = _InitOpDefLibrary(b"\nj\n\013EagerPyFunc\022\014\n\005input2\003Tin\032\016\n\006output2\004Tout\"\017\n\005token\022\006string\"\023\n\003Tin\022\nlist(type)(\001\"\024\n\004Tout\022\nlist(type)(\001\210\001\001\ne\n\006PyFunc\022\014\n\005input2\003Tin\032\016\n\006output2\004Tout\"\017\n\005token\022\006string\"\023\n\003Tin\022\nlist(type)(\001\"\024\n\004Tout\022\nlist(type)(\001\210\001\001\nk\n\017PyFuncStateless\022\014\n\005input2\003Tin\032\016\n\006output2\004Tout\"\017\n\005token\022\006string\"\023\n\003Tin\022\nlist(type)(\001\"\024\n\004Tout\022\nlist(type)(\001")
  