text: # Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  # Tests for this file live in python/kernel_tests/array_ops_test.py
  """Support for manipulating tensors.
  
  See the [Array Ops](https://tensorflow.org/api_guides/python/array_ops) guide.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import sys
  
  import numpy as np
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import common_shapes
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import sparse_tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  # 'Constant' gets imported in the module 'array_ops'.
  from tensorflow.python.framework.constant_op import constant
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import gen_math_ops
  # go/tf-wildcard-import
  # pylint: disable=wildcard-import
  from tensorflow.python.ops.gen_array_ops import *
  from tensorflow.python.ops.gen_array_ops import reverse_v2 as reverse  # pylint: disable=unused-import
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import tf_export
  # pylint: enable=wildcard-import
  
  # Used for slicing to specify a new 1 size dimension
  newaxis = None
  tf_export("newaxis").export_constant(__name__, "newaxis")
  
  # We override the 'slice' for the "slice" op, so we keep python's
  # existing 'slice' for later use in this module.
  _BaseSlice = slice
  
  
  @tf_export("identity")
  def identity(input, name=None):  # pylint: disable=redefined-builtin
    r"""Return a tensor with the same shape and contents as input.
  
    Args:
      input: A `Tensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`. Has the same type as `input`.
    """
    if context.executing_eagerly():
      input = ops.convert_to_tensor(input)
      in_device = input.device
      # TODO(ashankar): Does 'identity' need to invoke execution callbacks?
      context_device = context.context().device_name
      if not context_device:
        context_device = "/job:localhost/replica:0/task:0/device:CPU:0"
      if context_device != in_device:
        return input._copy()  # pylint: disable=protected-access
      return input
    else:
      return gen_array_ops.identity(input, name=name)
  
  
  # pylint: disable=redefined-builtin,protected-access
  @tf_export("expand_dims")
  @deprecation.deprecated_args(None, "Use the `axis` argument instead", "dim")
  def expand_dims(input, axis=None, name=None, dim=None):
    """Inserts a dimension of 1 into a tensor's shape.
  
    Given a tensor `input`, this operation inserts a dimension of 1 at the
    dimension index `axis` of `input`'s shape. The dimension index `axis` starts
    at zero; if you specify a negative number for `axis` it is counted backward
    from the end.
  
    This operation is useful if you want to add a batch dimension to a single
    element. For example, if you have a single image of shape `[height, width,
    channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`,
    which will make the shape `[1, height, width, channels]`.
  
    Other examples:
  
    ```python
    # 't' is a tensor of shape [2]
    tf.shape(tf.expand_dims(t, 0))  # [1, 2]
    tf.shape(tf.expand_dims(t, 1))  # [2, 1]
    tf.shape(tf.expand_dims(t, -1))  # [2, 1]
  
    # 't2' is a tensor of shape [2, 3, 5]
    tf.shape(tf.expand_dims(t2, 0))  # [1, 2, 3, 5]
    tf.shape(tf.expand_dims(t2, 2))  # [2, 3, 1, 5]
    tf.shape(tf.expand_dims(t2, 3))  # [2, 3, 5, 1]
    ```
  
    This operation requires that:
  
    `-1-input.dims() <= dim <= input.dims()`
  
    This operation is related to `squeeze()`, which removes dimensions of
    size 1.
  
    Args:
      input: A `Tensor`.
      axis: 0-D (scalar). Specifies the dimension index at which to
        expand the shape of `input`. Must be in the range
        `[-rank(input) - 1, rank(input)]`.
      name: The name of the output `Tensor`.
      dim: 0-D (scalar). Equivalent to `axis`, to be deprecated.
  
    Returns:
      A `Tensor` with the same data as `input`, but its shape has an additional
      dimension of size 1 added.
  
    Raises:
      ValueError: if both `dim` and `axis` are specified.
    """
    axis = deprecation.deprecated_argument_lookup("axis", axis, "dim", dim)
    return gen_array_ops.expand_dims(input, axis, name)
  
  
  # pylint: enable=redefined-builtin,protected-access
  
  
  # Aliases for some automatically-generated names.
  # pylint: disable=protected-access
  @deprecation.deprecated(
      "2016-11-30",
      "This op will be removed after the deprecation date. "
      "Please switch to tf.setdiff1d().")
  def listdiff(x, y, out_idx=None, name=None):
    return gen_array_ops.list_diff(x, y, out_idx, name)
  
  
  listdiff.__doc__ = gen_array_ops.list_diff.__doc__ + "\n" + listdiff.__doc__
  
  # pylint: enable=protected-access
  
  
  # pylint: disable=undefined-variable
  @tf_export("setdiff1d")
  def setdiff1d(x, y, index_dtype=dtypes.int32, name=None):
    return gen_array_ops.list_diff(x, y, index_dtype, name)
  
  
  setdiff1d.__doc__ = gen_array_ops.list_diff.__doc__
  
  
  @tf_export("broadcast_dynamic_shape")
  def broadcast_dynamic_shape(shape_x, shape_y):
    """Returns the broadcasted dynamic shape between `shape_x` and `shape_y`.
  
    Args:
      shape_x: A rank 1 integer `Tensor`, representing the shape of x.
      shape_y: A rank 1 integer `Tensor`, representing the shape of y.
  
    Returns:
      A rank 1 integer `Tensor` representing the broadcasted shape.
    """
    return gen_array_ops.broadcast_args(shape_x, shape_y)
  
  
  @tf_export("broadcast_static_shape")
  def broadcast_static_shape(shape_x, shape_y):
    """Returns the broadcasted static shape between `shape_x` and `shape_y`.
  
    Args:
      shape_x: A `TensorShape`
      shape_y: A `TensorShape`
  
    Returns:
      A `TensorShape` representing the broadcasted shape.
  
    Raises:
      ValueError: If the two shapes can not be broadcasted.
    """
    return common_shapes.broadcast_shape(shape_x, shape_y)
  
  
  @tf_export("shape")
  def shape(input, name=None, out_type=dtypes.int32):
    # pylint: disable=redefined-builtin
    """Returns the shape of a tensor.
  
    This operation returns a 1-D integer tensor representing the shape of `input`.
  
    For example:
  
    ```python
    t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
    tf.shape(t)  # [2, 2, 3]
    ```
  
    Args:
      input: A `Tensor` or `SparseTensor`.
      name: A name for the operation (optional).
      out_type: (Optional) The specified output type of the operation
        (`int32` or `int64`). Defaults to `tf.int32`.
  
    Returns:
      A `Tensor` of type `out_type`.
    """
    return shape_internal(input, name, optimize=True, out_type=out_type)
  
  
  def shape_internal(input, name=None, optimize=True, out_type=dtypes.int32):
    # pylint: disable=redefined-builtin
    """Returns the shape of a tensor.
  
    Args:
      input: A `Tensor` or `SparseTensor`.
      name: A name for the operation (optional).
      optimize: if true, encode the shape as a constant when possible.
      out_type: (Optional) The specified output type of the operation
        (`int32` or `int64`). Defaults to tf.int32.
  
    Returns:
      A `Tensor` of type `out_type`.
  
    """
    with ops.name_scope(name, "Shape", [input]) as name:
      if isinstance(input, (sparse_tensor.SparseTensor,
                            sparse_tensor.SparseTensorValue)):
        return gen_math_ops.cast(input.dense_shape, out_type)
      else:
        if not context.executing_eagerly():
          input_tensor = ops.convert_to_tensor(input)
          input_shape = input_tensor.get_shape()
          if optimize and input_shape.is_fully_defined():
            return constant(input_shape.as_list(), out_type, name=name)
        return gen_array_ops.shape(input, name=name, out_type=out_type)
  
  
  @tf_export("shape_n")
  def shape_n(input, out_type=dtypes.int32, name=None):
    # pylint: disable=redefined-builtin
    """Returns shape of tensors.
  
    Args:
      input: A list of at least 1 `Tensor` object with the same type.
      out_type: The specified output type of the operation
        (`int32` or `int64`). Defaults to `tf.int32`(optional).
      name: A name for the operation (optional).
  
    Returns:
      A list with the same length as `input` of `Tensor` objects with
        type `out_type`.
    """
  
    return gen_array_ops.shape_n(input, out_type=out_type, name=name)
  
  
  @tf_export("size")
  def size(input, name=None, out_type=dtypes.int32):
    # pylint: disable=redefined-builtin
    """Returns the size of a tensor.
  
    Returns a 0-D `Tensor` representing the number of elements in `input`
    of type `out_type`. Defaults to tf.int32.
  
    For example:
  
    ```python
    t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
    tf.size(t)  # 12
    ```
  
    Args:
      input: A `Tensor` or `SparseTensor`.
      name: A name for the operation (optional).
      out_type: (Optional) The specified non-quantized numeric output type
        of the operation. Defaults to `tf.int32`.
  
    Returns:
      A `Tensor` of type `out_type`. Defaults to `tf.int32`.
  
    @compatibility(numpy)
    Equivalent to np.size()
    @end_compatibility
    """
    return size_internal(input, name, optimize=True, out_type=out_type)
  
  
  def size_internal(input, name=None, optimize=True, out_type=dtypes.int32):
    # pylint: disable=redefined-builtin,protected-access
    """Returns the size of a tensor.
  
    Args:
      input: A `Tensor` or `SparseTensor`.
      name: A name for the operation (optional).
      optimize: if true, encode the size as a constant when possible.
      out_type: (Optional) The specified non-quantized numeric output type
        of the operation. Defaults to `tf.int32`.
  
    Returns:
      A `Tensor` of type `out_type`. Defaults to `tf.int32`.
    """
    if context.executing_eagerly() and not isinstance(
        input, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):
      input = ops.convert_to_tensor(input)
      np_out_type = out_type.as_numpy_dtype
      num_elements = np.prod(input._shape_tuple(), dtype=np_out_type)  # pylint: disable=protected-acces:
      return ops.convert_to_tensor(num_elements, dtype=out_type)
    with ops.name_scope(name, "Size", [input]) as name:
      if isinstance(input, (sparse_tensor.SparseTensor,
                            sparse_tensor.SparseTensorValue)):
        return gen_math_ops.prod(
            gen_math_ops.cast(input.dense_shape, out_type), 0, name=name)
      else:
        input_tensor = ops.convert_to_tensor(input)
        input_shape = input_tensor.get_shape()
        if optimize:
          if input_shape.is_fully_defined():
            return constant(input_shape.num_elements(), out_type, name=name)
          if input_shape.dims and any(dim == 0 for dim in input_shape.dims):
            return constant(0, out_type, name=name)
        return gen_array_ops.size(input, name=name, out_type=out_type)
  
  
  @tf_export("rank")
  def rank(input, name=None):
    # pylint: disable=redefined-builtin
    """Returns the rank of a tensor.
  
    Returns a 0-D `int32` `Tensor` representing the rank of `input`.
  
    For example:
  
    ```python
    # shape of tensor 't' is [2, 2, 3]
    t = tf.constant([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]]])
    tf.rank(t)  # 3
    ```
  
    **Note**: The rank of a tensor is not the same as the rank of a matrix. The
    rank of a tensor is the number of indices required to uniquely select each
    element of the tensor. Rank is also known as "order", "degree", or "ndims."
  
    Args:
      input: A `Tensor` or `SparseTensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` of type `int32`.
  
    @compatibility(numpy)
    Equivalent to np.ndim
    @end_compatibility
    """
    return rank_internal(input, name, optimize=True)
  
  
  def rank_internal(input, name=None, optimize=True):
    # pylint: disable=redefined-builtin
    """Returns the rank of a tensor.
  
    Args:
      input: A `Tensor` or `SparseTensor`.
      name: A name for the operation (optional).
      optimize: if true, encode the rank as a constant when possible.
  
    Returns:
      A `Tensor` of type `int32`.
    """
    with ops.name_scope(name, "Rank", [input]) as name:
      if isinstance(input, (sparse_tensor.SparseTensor,
                            sparse_tensor.SparseTensorValue)):
        return gen_array_ops.size(input.dense_shape, name=name)
      else:
        input_tensor = ops.convert_to_tensor(input)
        input_shape = input_tensor.get_shape()
        if optimize and input_shape.ndims is not None:
          return constant(input_shape.ndims, dtypes.int32, name=name)
        return gen_array_ops.rank(input, name=name)
  
  
  def _slice_helper(tensor, slice_spec, var=None):
    """Overload for Tensor.__getitem__.
  
    This operation extracts the specified region from the tensor.
    The notation is similar to NumPy with the restriction that
    currently only support basic indexing. That means that
    using a non-scalar tensor as input is not currently allowed.
  
    Some useful examples:
  
    ```python
    # strip leading and trailing 2 elements
    foo = tf.constant([1,2,3,4,5,6])
    print(foo[2:-2].eval())  # => [3,4]
  
    # skip every row and reverse every column
    foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
    print(foo[::2,::-1].eval())  # => [[3,2,1], [9,8,7]]
  
    # Use scalar tensors as indices on both dimensions
    print(foo[tf.constant(0), tf.constant(2)].eval())  # => 3
  
    # Insert another dimension
    foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
    print(foo[tf.newaxis, :, :].eval()) # => [[[1,2,3], [4,5,6], [7,8,9]]]
    print(foo[:, tf.newaxis, :].eval()) # => [[[1,2,3]], [[4,5,6]], [[7,8,9]]]
    print(foo[:, :, tf.newaxis].eval()) # => [[[1],[2],[3]], [[4],[5],[6]],
    [[7],[8],[9]]]
  
    # Ellipses (3 equivalent operations)
    foo = tf.constant([[1,2,3], [4,5,6], [7,8,9]])
    print(foo[tf.newaxis, :, :].eval())  # => [[[1,2,3], [4,5,6], [7,8,9]]]
    print(foo[tf.newaxis, ...].eval())  # => [[[1,2,3], [4,5,6], [7,8,9]]]
    print(foo[tf.newaxis].eval())  # => [[[1,2,3], [4,5,6], [7,8,9]]]
    ```
  
    Notes:
      - `tf.newaxis` is `None` as in NumPy.
      - An implicit ellipsis is placed at the end of the `slice_spec`
      - NumPy advanced indexing is currently not supported.
  
    Args:
      tensor: An ops.Tensor object.
      slice_spec: The arguments to Tensor.__getitem__.
      var: In the case of variable slice assignment, the Variable
        object to slice (i.e. tensor is the read-only view of this
        variable).
  
    Returns:
      The appropriate slice of "tensor", based on "slice_spec".
  
    Raises:
      ValueError: If a slice range is negative size.
      TypeError: If the slice indices aren't int, slice, or Ellipsis.
    """
  
    if not isinstance(slice_spec, (list, tuple)):
      slice_spec = [slice_spec]
  
    begin, end, strides = [], [], []
    index = 0
  
    new_axis_mask, shrink_axis_mask = 0, 0
    begin_mask, end_mask = 0, 0
    ellipsis_mask = 0
    for s in slice_spec:
      if isinstance(s, _BaseSlice):
        # python doesn't always use None when constructing ranges
        # for example a[:] gives slice(None,sys.maxsize,None)
        # whereas a[::1] gives slice(None,None,None)
        if s.start is not None and s.start is not sys.maxsize:
          begin.append(s.start)
        else:
          begin.append(0)
          begin_mask |= (1 << index)
        if s.stop is not None and s.stop != sys.maxsize:
          end.append(s.stop)
        else:
          end.append(0)
          end_mask |= (1 << index)
        if s.step is not None:
          strides.append(s.step)
        else:
          strides.append(1)
      elif s is Ellipsis:
        begin.append(0)
        end.append(0)
        strides.append(1)
        ellipsis_mask |= (1 << index)
      elif s is newaxis:
        begin.append(0)
        end.append(0)
        strides.append(1)
        new_axis_mask |= (1 << index)
      else:
        begin.append(s)
        end.append(s + 1)
        strides.append(1)
        shrink_axis_mask |= (1 << index)
      index += 1
  
    # stack possibly involves no tensors, so we must use op_scope correct graph.
    with ops.name_scope(None, "strided_slice",
                        [tensor] + begin + end + strides) as name:
      if begin:
        packed_begin, packed_end, packed_strides = (stack(begin), stack(end),
                                                    stack(strides))
        if (packed_begin.dtype == dtypes.int64 or
            packed_end.dtype == dtypes.int64 or
            packed_strides.dtype == dtypes.int64):
          if packed_begin.dtype != dtypes.int64:
            packed_begin = gen_math_ops.cast(packed_begin, dtypes.int64)
          if packed_end.dtype != dtypes.int64:
            packed_end = gen_math_ops.cast(packed_end, dtypes.int64)
          if packed_strides.dtype != dtypes.int64:
            packed_strides = gen_math_ops.cast(packed_strides, dtypes.int64)
      else:
        var_empty = constant([], dtype=dtypes.int32)
        packed_begin = packed_end = packed_strides = var_empty
      return strided_slice(
          tensor,
          packed_begin,
          packed_end,
          packed_strides,
          begin_mask=begin_mask,
          end_mask=end_mask,
          shrink_axis_mask=shrink_axis_mask,
          new_axis_mask=new_axis_mask,
          ellipsis_mask=ellipsis_mask,
          var=var,
          name=name)
  
  
  # pylint: disable=undefined-variable,protected-access,redefined-outer-name
  @tf_export("slice")
  def slice(input_, begin, size, name=None):
    # pylint: disable=redefined-builtin
    """Extracts a slice from a tensor.
  
    This operation extracts a slice of size `size` from a tensor `input` starting
    at the location specified by `begin`. The slice `size` is represented as a
    tensor shape, where `size[i]` is the number of elements of the 'i'th dimension
    of `input` that you want to slice. The starting location (`begin`) for the
    slice is represented as an offset in each dimension of `input`. In other
    words, `begin[i]` is the offset into the 'i'th dimension of `input` that you
    want to slice from.
  
    Note that `tf.Tensor.__getitem__` is typically a more pythonic way to
    perform slices, as it allows you to write `foo[3:7, :-2]` instead of
    `tf.slice(foo, [3, 0], [4, foo.get_shape()[1]-2])`.
  
    `begin` is zero-based; `size` is one-based. If `size[i]` is -1,
    all remaining elements in dimension i are included in the
    slice. In other words, this is equivalent to setting:
  
    `size[i] = input.dim_size(i) - begin[i]`
  
    This operation requires that:
  
    `0 <= begin[i] <= begin[i] + size[i] <= Di  for i in [0, n]`
  
    For example:
  
    ```python
    t = tf.constant([[[1, 1, 1], [2, 2, 2]],
                     [[3, 3, 3], [4, 4, 4]],
                     [[5, 5, 5], [6, 6, 6]]])
    tf.slice(t, [1, 0, 0], [1, 1, 3])  # [[[3, 3, 3]]]
    tf.slice(t, [1, 0, 0], [1, 2, 3])  # [[[3, 3, 3],
                                       #   [4, 4, 4]]]
    tf.slice(t, [1, 0, 0], [2, 1, 3])  # [[[3, 3, 3]],
                                       #  [[5, 5, 5]]]
    ```
  
    Args:
      input_: A `Tensor`.
      begin: An `int32` or `int64` `Tensor`.
      size: An `int32` or `int64` `Tensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` the same type as `input`.
    """
    return gen_array_ops._slice(input_, begin, size, name=name)
  
  
  # pylint: disable=invalid-name
  @tf_export("strided_slice")
  def strided_slice(input_,
                    begin,
                    end,
                    strides=None,
                    begin_mask=0,
                    end_mask=0,
                    ellipsis_mask=0,
                    new_axis_mask=0,
                    shrink_axis_mask=0,
                    var=None,
                    name=None):
    """Extracts a strided slice of a tensor (generalized python array indexing).
  
    **Instead of calling this op directly most users will want to use the
    NumPy-style slicing syntax (e.g. `tensor[..., 3:4:-1, tf.newaxis, 3]`), which
    is supported via `tf.Tensor.__getitem__` and `tf.Variable.__getitem__`.**
    The interface of this op is a low-level encoding of the slicing syntax.
  
    Roughly speaking, this op extracts a slice of size `(end-begin)/stride`
    from the given `input_` tensor. Starting at the location specified by `begin`
    the slice continues by adding `stride` to the index until all dimensions are
    not less than `end`.
    Note that a stride can be negative, which causes a reverse slice.
  
    Given a Python slice `input[spec0, spec1, ..., specn]`,
    this function will be called as follows.
  
    `begin`, `end`, and `strides` will be vectors of length n.
    n in general is not equal to the rank of the `input_` tensor.
  
    In each mask field (`begin_mask`, `end_mask`, `ellipsis_mask`,
    `new_axis_mask`, `shrink_axis_mask`) the ith bit will correspond to
    the ith spec.
  
    If the ith bit of `begin_mask` is set, `begin[i]` is ignored and
    the fullest possible range in that dimension is used instead.
    `end_mask` works analogously, except with the end range.
  
    `foo[5:,:,:3]` on a 7x8x9 tensor is equivalent to `foo[5:7,0:8,0:3]`.
    `foo[::-1]` reverses a tensor with shape 8.
  
    If the ith bit of `ellipsis_mask` is set, as many unspecified dimensions
    as needed will be inserted between other dimensions. Only one
    non-zero bit is allowed in `ellipsis_mask`.
  
    For example `foo[3:5,...,4:5]` on a shape 10x3x3x10 tensor is
    equivalent to `foo[3:5,:,:,4:5]` and
    `foo[3:5,...]` is equivalent to `foo[3:5,:,:,:]`.
  
    If the ith bit of `new_axis_mask` is set, then `begin`,
    `end`, and `stride` are ignored and a new length 1 dimension is
    added at this point in the output tensor.
  
    For example,
    `foo[:4, tf.newaxis, :2]` would produce a shape `(4, 1, 2)` tensor.
  
    If the ith bit of `shrink_axis_mask` is set, it implies that the ith
    specification shrinks the dimensionality by 1, taking on the value at index
    `begin[i]`. `end[i]` and `strides[i]` are ignored in this case. For example in
    Python one might do `foo[:, 3, :]` which would result in `shrink_axis_mask`
    equal to 2.
  
  
    NOTE: `begin` and `end` are zero-indexed.
    `strides` entries must be non-zero.
  
  
    ```python
    t = tf.constant([[[1, 1, 1], [2, 2, 2]],
                     [[3, 3, 3], [4, 4, 4]],
                     [[5, 5, 5], [6, 6, 6]]])
    tf.strided_slice(t, [1, 0, 0], [2, 1, 3], [1, 1, 1])  # [[[3, 3, 3]]]
    tf.strided_slice(t, [1, 0, 0], [2, 2, 3], [1, 1, 1])  # [[[3, 3, 3],
                                                          #   [4, 4, 4]]]
    tf.strided_slice(t, [1, -1, 0], [2, -3, 3], [1, -1, 1])  # [[[4, 4, 4],
                                                             #   [3, 3, 3]]]
    ```
  
    Args:
      input_: A `Tensor`.
      begin: An `int32` or `int64` `Tensor`.
      end: An `int32` or `int64` `Tensor`.
      strides: An `int32` or `int64` `Tensor`.
      begin_mask: An `int32` mask.
      end_mask: An `int32` mask.
      ellipsis_mask: An `int32` mask.
      new_axis_mask: An `int32` mask.
      shrink_axis_mask: An `int32` mask.
      var: The variable corresponding to `input_` or None
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` the same type as `input`.
    """
  
    if strides is None:
      strides = ones_like(begin)
  
    op = gen_array_ops.strided_slice(
        input=input_,
        begin=begin,
        end=end,
        strides=strides,
        name=name,
        begin_mask=begin_mask,
        end_mask=end_mask,
        ellipsis_mask=ellipsis_mask,
        new_axis_mask=new_axis_mask,
        shrink_axis_mask=shrink_axis_mask)
  
    parent_name = name
  
    if not (var is None and isinstance(op, ops.EagerTensor)):
      def assign(val, name=None):
        """Closure that holds all the arguments to create an assignment."""
  
        if var is None:
          raise ValueError("Sliced assignment is only supported for variables")
  
        if name is None:
          name = parent_name + "_assign"
  
        return var._strided_slice_assign(
            begin=begin,
            end=end,
            strides=strides,
            value=val,
            name=name,
            begin_mask=begin_mask,
            end_mask=end_mask,
            ellipsis_mask=ellipsis_mask,
            new_axis_mask=new_axis_mask,
            shrink_axis_mask=shrink_axis_mask)
  
      op.assign = assign
    return op
  
  
  def _SliceHelperVar(var, slice_spec):
    """Creates a slice helper object given a variable.
  
    This allows creating a sub-tensor from part of the current contents
    of a variable. See `tf.Tensor.__getitem__` for detailed examples
    of slicing.
  
    This function in addition also allows assignment to a sliced range.
    This is similar to `__setitem__` functionality in Python. However,
    the syntax is different so that the user can capture the assignment
    operation for grouping or passing to `sess.run()`.
    For example,
  
    ```python
    import tensorflow as tf
    A = tf.Variable([[1,2,3], [4,5,6], [7,8,9]], dtype=tf.float32)
    with tf.Session() as sess:
      sess.run(tf.global_variables_initializer())
      print(sess.run(A[:2, :2]))  # => [[1,2], [4,5]]
  
      op = A[:2,:2].assign(22. * tf.ones((2, 2)))
      print(sess.run(op))  # => [[22, 22, 3], [22, 22, 6], [7,8,9]]
    ```
  
    Note that assignments currently do not support NumPy broadcasting
    semantics.
  
    Args:
      var: An `ops.Variable` object.
      slice_spec: The arguments to `Tensor.__getitem__`.
  
    Returns:
      The appropriate slice of "tensor", based on "slice_spec".
      As an operator. The operator also has a `assign()` method
      that can be used to generate an assignment operator.
  
    Raises:
      ValueError: If a slice range is negative size.
      TypeError: If the slice indices aren't int, slice, or Ellipsis.
  
    """
  
    return _slice_helper(var._AsTensor(), slice_spec, var)
  
  
  ops.Tensor._override_operator("__getitem__", _slice_helper)
  
  
  @tf_export("parallel_stack")
  def parallel_stack(values, name="parallel_stack"):
    """Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor in parallel.
  
    Requires that the shape of inputs be known at graph construction time.
  
    Packs the list of tensors in `values` into a tensor with rank one higher than
    each tensor in `values`, by packing them along the first dimension.
    Given a list of length `N` of tensors of shape `(A, B, C)`; the `output`
    tensor will have the shape `(N, A, B, C)`.
  
    For example:
  
    ```python
    x = tf.constant([1, 4])
    y = tf.constant([2, 5])
    z = tf.constant([3, 6])
    tf.parallel_stack([x, y, z])  # [[1, 4], [2, 5], [3, 6]]
    ```
  
    The difference between `stack` and `parallel_stack` is that `stack` requires
    all the inputs be computed before the operation will begin but doesn't require
    that the input shapes be known during graph construction.
  
    `parallel_stack` will copy pieces of the input into the output as they become
    available, in some situations this can provide a performance benefit.
  
    Unlike `stack`, `parallel_stack` does NOT support backpropagation.
  
    This is the opposite of unstack.  The numpy equivalent is
  
        tf.parallel_stack([x, y, z]) = np.asarray([x, y, z])
  
    Args:
      values: A list of `Tensor` objects with the same shape and type.
      name: A name for this operation (optional).
  
    Returns:
      output: A stacked `Tensor` with the same type as `values`.
    """
    with ops.name_scope(name):
      value_t = ops.convert_to_tensor(values[0])
      value_shape = ops.convert_to_tensor(value_t).get_shape()
  
      output_shape = tensor_shape.TensorShape([len(values)])
      output_shape = output_shape.concatenate(value_shape)
      # expand_dims converts concat to stack.
      return gen_array_ops.parallel_concat(
          [expand_dims(value, 0) for value in values], shape=output_shape)
  
  
  @tf_export("stack")
  def stack(values, axis=0, name="stack"):
    """Stacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.
  
    Packs the list of tensors in `values` into a tensor with rank one higher than
    each tensor in `values`, by packing them along the `axis` dimension.
    Given a list of length `N` of tensors of shape `(A, B, C)`;
  
    if `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.
    if `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.
    Etc.
  
    For example:
  
    ```python
    x = tf.constant([1, 4])
    y = tf.constant([2, 5])
    z = tf.constant([3, 6])
    tf.stack([x, y, z])  # [[1, 4], [2, 5], [3, 6]] (Pack along first dim.)
    tf.stack([x, y, z], axis=1)  # [[1, 2, 3], [4, 5, 6]]
    ```
  
    This is the opposite of unstack.  The numpy equivalent is
  
    ```python
    tf.stack([x, y, z]) = np.stack([x, y, z])
    ```
  
    Args:
      values: A list of `Tensor` objects with the same shape and type.
      axis: An `int`. The axis to stack along. Defaults to the first dimension.
        Negative values wrap around, so the valid range is `[-(R+1), R+1)`.
      name: A name for this operation (optional).
  
    Returns:
      output: A stacked `Tensor` with the same type as `values`.
  
    Raises:
      ValueError: If `axis` is out of the range [-(R+1), R+1).
    """
    if axis == 0:
      try:
        # If the input is a constant list, it can be converted to a constant op
        return ops.convert_to_tensor(values, name=name)
      except (TypeError, ValueError):
        pass  # Input list contains non-constant tensors
  
    value_shape = ops.convert_to_tensor(values[0], name=name)._shape_tuple()  # pylint: disable=protected-access
    if value_shape is not None:
      expanded_num_dims = len(value_shape) + 1
      if axis < -expanded_num_dims or axis >= expanded_num_dims:
        raise ValueError("axis = %d not in [%d, %d)" % (axis, -expanded_num_dims,
                                                        expanded_num_dims))
  
    return gen_array_ops.pack(values, axis=axis, name=name)
  
  
  # pylint: disable=invalid-name
  def _autopacking_helper(list_or_tuple, dtype, name):
    """Converts the given list or tuple to a tensor by packing.
  
    Args:
      list_or_tuple: A (possibly nested) list or tuple containing a tensor.
      dtype: The element type of the returned tensor.
      name: A name for the returned tensor.
  
    Returns:
      A `tf.Tensor` with value equivalent to `list_or_tuple`.
    """
    if context.executing_eagerly():
      # NOTE: Fast path when all the items are tensors, this doesn't do any type
      # checking.
      if all(ops.is_dense_tensor_like(elem) for elem in list_or_tuple):
        return gen_array_ops.pack(list_or_tuple, name=name)
    must_pack = False
    converted_elems = []
    with ops.name_scope(name) as scope:
      for i, elem in enumerate(list_or_tuple):
        if ops.is_dense_tensor_like(elem):
          if dtype is not None and elem.dtype.base_dtype != dtype:
            raise TypeError("Cannot convert a list containing a tensor of dtype "
                            "%s to %s (Tensor is: %r)" % (elem.dtype, dtype,
                                                          elem))
          converted_elems.append(elem)
          must_pack = True
        elif isinstance(elem, (list, tuple)):
          converted_elem = _autopacking_helper(elem, dtype, str(i))
          if ops.is_dense_tensor_like(converted_elem):
            must_pack = True
          converted_elems.append(converted_elem)
        else:
          converted_elems.append(elem)
      if must_pack:
        elems_as_tensors = []
        for i, elem in enumerate(converted_elems):
          if ops.is_dense_tensor_like(elem):
            elems_as_tensors.append(elem)
          else:
            # NOTE(mrry): This is inefficient, but it enables us to
            # handle the case where the list arguments are other
            # convertible-to-tensor types, such as numpy arrays.
            elems_as_tensors.append(
                constant_op.constant(elem, dtype=dtype, name=str(i)))
        return gen_array_ops.pack(elems_as_tensors, name=scope)
      else:
        return converted_elems
  
  
  def _get_dtype_from_nested_lists(list_or_tuple):
    """Returns the dtype of any tensor-like object in `list_or_tuple`, if found.
  
    Args:
      list_or_tuple: A list or tuple representing an object that can be
        converted to a `tf.Tensor`.
  
    Returns:
      The dtype of any tensor-like object in `list_or_tuple`, or `None` if no
      such object exists.
    """
    for elem in list_or_tuple:
      if ops.is_dense_tensor_like(elem):
        return elem.dtype.base_dtype
      elif isinstance(elem, (list, tuple)):
        maybe_dtype = _get_dtype_from_nested_lists(elem)
        if maybe_dtype is not None:
          return maybe_dtype
    return None
  
  
  def _cast_nested_seqs_to_dtype(dtype):
    def _maybe_cast(elem):
      if ops.is_dense_tensor_like(elem):
        if dtype != elem.dtype.base_dtype:
          elem = gen_math_ops.cast(elem, dtype)
      return elem
    return _maybe_cast
  
  
  def _autopacking_conversion_function(v, dtype=None, name=None, as_ref=False):
    """Tensor conversion function that automatically packs arguments."""
    if as_ref:
      return NotImplemented
    inferred_dtype = _get_dtype_from_nested_lists(v)
    if inferred_dtype is None:
      # We did not find any tensor-like objects in the nested lists, so defer to
      # other conversion functions.
      return NotImplemented
    if dtype is None:
      dtype = inferred_dtype
    elif dtype != inferred_dtype:
      v = nest.map_structure(_cast_nested_seqs_to_dtype(dtype), v)
    return _autopacking_helper(v, dtype, name or "packed")
  
  
  # pylint: enable=invalid-name
  
  # NOTE: Register this conversion function to run *before* one that
  # assumes every element is a value.
  ops.register_tensor_conversion_function((list, tuple),
                                          _autopacking_conversion_function, 99)
  
  
  @tf_export("unstack")
  def unstack(value, num=None, axis=0, name="unstack"):
    """Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
  
    Unpacks `num` tensors from `value` by chipping it along the `axis` dimension.
    If `num` is not specified (the default), it is inferred from `value`'s shape.
    If `value.shape[axis]` is not known, `ValueError` is raised.
  
    For example, given a tensor of shape `(A, B, C, D)`;
  
    If `axis == 0` then the i'th tensor in `output` is the slice
      `value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`.
      (Note that the dimension unpacked along is gone, unlike `split`).
  
    If `axis == 1` then the i'th tensor in `output` is the slice
      `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`.
    Etc.
  
    This is the opposite of stack.
  
    Args:
      value: A rank `R > 0` `Tensor` to be unstacked.
      num: An `int`. The length of the dimension `axis`. Automatically inferred
        if `None` (the default).
      axis: An `int`. The axis to unstack along. Defaults to the first
        dimension. Negative values wrap around, so the valid range is `[-R, R)`.
      name: A name for the operation (optional).
  
    Returns:
      The list of `Tensor` objects unstacked from `value`.
  
    Raises:
      ValueError: If `num` is unspecified and cannot be inferred.
      ValueError: If `axis` is out of the range [-R, R).
    """
    if num is None:
      value = ops.convert_to_tensor(value)
      value_shape = value.get_shape()
      if value_shape.ndims is not None:
        if axis < -value_shape.ndims or axis >= value_shape.ndims:
          raise ValueError("axis = %d not in [%d, %d)" %
                           (axis, -value_shape.ndims, value_shape.ndims))
        num = value_shape[axis].value
    if num is None:
      raise ValueError("Cannot infer num from shape %s" % value_shape)
    return gen_array_ops.unpack(value, num=num, axis=axis, name=name)
  
  
  @tf_export("concat")
  def concat(values, axis, name="concat"):
    """Concatenates tensors along one dimension.
  
    Concatenates the list of tensors `values` along dimension `axis`.  If
    `values[i].shape = [D0, D1, ... Daxis(i), ...Dn]`, the concatenated
    result has shape
  
        [D0, D1, ... Raxis, ...Dn]
  
    where
  
        Raxis = sum(Daxis(i))
  
    That is, the data from the input tensors is joined along the `axis`
    dimension.
  
    The number of dimensions of the input tensors must match, and all dimensions
    except `axis` must be equal.
  
    For example:
  
    ```python
    t1 = [[1, 2, 3], [4, 5, 6]]
    t2 = [[7, 8, 9], [10, 11, 12]]
    tf.concat([t1, t2], 0)  # [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
    tf.concat([t1, t2], 1)  # [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]]
  
    # tensor t3 with shape [2, 3]
    # tensor t4 with shape [2, 3]
    tf.shape(tf.concat([t3, t4], 0))  # [4, 3]
    tf.shape(tf.concat([t3, t4], 1))  # [2, 6]
    ```
    As in Python, the `axis` could also be negative numbers. Negative `axis`
    are interpreted as counting from the end of the rank, i.e.,
     `axis + rank(values)`-th dimension.
  
    For example:
  
    ```python
    t1 = [[[1, 2], [2, 3]], [[4, 4], [5, 3]]]
    t2 = [[[7, 4], [8, 4]], [[2, 10], [15, 11]]]
    tf.concat([t1, t2], -1)
    ```
  
    would produce:
  
    ```python
    [[[ 1,  2,  7,  4],
      [ 2,  3,  8,  4]],
  
     [[ 4,  4,  2, 10],
      [ 5,  3, 15, 11]]]
    ```
  
    Note: If you are concatenating along a new axis consider using stack.
    E.g.
  
    ```python
    tf.concat([tf.expand_dims(t, axis) for t in tensors], axis)
    ```
  
    can be rewritten as
  
    ```python
    tf.stack(tensors, axis=axis)
    ```
  
    Args:
      values: A list of `Tensor` objects or a single `Tensor`.
      axis: 0-D `int32` `Tensor`.  Dimension along which to concatenate. Must be
        in the range `[-rank(values), rank(values))`. As in Python, indexing
        for axis is 0-based. Positive axis in the rage of
        `[0, rank(values))` refers to `axis`-th dimension. And negative axis
        refers to `axis + rank(values)`-th dimension.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` resulting from concatenation of the input tensors.
    """
    if not isinstance(values, (list, tuple)):
      values = [values]
    # TODO(mrry): Change to return values?
    if len(values) == 1:  # Degenerate case of one tensor.
      # Make a throwaway call to convert_to_tensor to make sure
      # that axis is of the correct type, and make sure that
      # the returned tensor is a scalar.
      # TODO(keveman): Implement a standalone type and shape checker.
      with ops.name_scope(name) as scope:
        ops.convert_to_tensor(
            axis, name="concat_dim",
            dtype=dtypes.int32).get_shape().assert_is_compatible_with(
                tensor_shape.scalar())
        return identity(values[0], name=scope)
    return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
  
  
  @tf_export("boolean_mask")
  def boolean_mask(tensor, mask, name="boolean_mask", axis=None):
    """Apply boolean mask to tensor.  Numpy equivalent is `tensor[mask]`.
  
    ```python
    # 1-D example
    tensor = [0, 1, 2, 3]
    mask = np.array([True, False, True, False])
    boolean_mask(tensor, mask)  # [0, 2]
    ```
  
    In general, `0 < dim(mask) = K <= dim(tensor)`, and `mask`'s shape must match
    the first K dimensions of `tensor`'s shape.  We then have:
      `boolean_mask(tensor, mask)[i, j1,...,jd] = tensor[i1,...,iK,j1,...,jd]`
    where `(i1,...,iK)` is the ith `True` entry of `mask` (row-major order).
    The `axis` could be used with `mask` to indicate the axis to mask from.
    In that case, `axis + dim(mask) <= dim(tensor)` and `mask`'s shape must match
    the first `axis + dim(mask)` dimensions of `tensor`'s shape.
  
    Args:
      tensor:  N-D tensor.
      mask:  K-D boolean tensor, K <= N and K must be known statically.
      name:  A name for this operation (optional).
      axis:  A 0-D int Tensor representing the axis in `tensor` to mask from.
        By default, axis is 0 which will mask from the first dimension. Otherwise
        K + axis <= N.
  
    Returns:
      (N-K+1)-dimensional tensor populated by entries in `tensor` corresponding
      to `True` values in `mask`.
  
    Raises:
      ValueError:  If shapes do not conform.
  
    Examples:
  
    ```python
    # 2-D example
    tensor = [[1, 2], [3, 4], [5, 6]]
    mask = np.array([True, False, True])
    boolean_mask(tensor, mask)  # [[1, 2], [5, 6]]
    ```
    """
  
    def _apply_mask_1d(reshaped_tensor, mask, axis=None):
      """Mask tensor along dimension 0 with a 1-D mask."""
      indices = squeeze(where(mask), axis=[1])
      return gather(reshaped_tensor, indices, axis=axis)
  
    with ops.name_scope(name, values=[tensor, mask]):
      tensor = ops.convert_to_tensor(tensor, name="tensor")
      mask = ops.convert_to_tensor(mask, name="mask")
  
      shape_mask = mask.get_shape()
      ndims_mask = shape_mask.ndims
      shape_tensor = tensor.get_shape()
      if ndims_mask == 0:
        raise ValueError("mask cannot be scalar.")
      if ndims_mask is None:
        raise ValueError(
            "Number of mask dimensions must be specified, even if some dimensions"
            " are None.  E.g. shape=[None] is ok, but shape=None is not.")
      axis = 0 if axis is None else axis
      shape_tensor[axis:axis + ndims_mask].assert_is_compatible_with(shape_mask)
  
      leading_size = gen_math_ops.prod(shape(tensor)[axis:axis + ndims_mask], [0])
      tensor = reshape(tensor,
                       concat([
                           shape(tensor)[:axis], [leading_size],
                           shape(tensor)[axis + ndims_mask:]
                       ], 0))
      first_dim = shape_tensor[axis:axis + ndims_mask].num_elements()
      tensor.set_shape(
          tensor_shape.as_shape(shape_tensor[:axis]).concatenate([first_dim])
          .concatenate(shape_tensor[axis + ndims_mask:]))
  
      mask = reshape(mask, [-1])
      return _apply_mask_1d(tensor, mask, axis)
  
  
  @tf_export("sparse.mask", "sparse_mask")
  @deprecation.deprecated_endpoints("sparse_mask")
  def sparse_mask(a, mask_indices, name=None):
    """Masks elements of `IndexedSlices`.
  
    Given an `IndexedSlices` instance `a`, returns another `IndexedSlices` that
    contains a subset of the slices of `a`. Only the slices at indices not
    specified in `mask_indices` are returned.
  
    This is useful when you need to extract a subset of slices in an
    `IndexedSlices` object.
  
    For example:
  
    ```python
    # `a` contains slices at indices [12, 26, 37, 45] from a large tensor
    # with shape [1000, 10]
    a.indices  # [12, 26, 37, 45]
    tf.shape(a.values)  # [4, 10]
  
    # `b` will be the subset of `a` slices at its second and third indices, so
    # we want to mask its first and last indices (which are at absolute
    # indices 12, 45)
    b = tf.sparse.mask(a, [12, 45])
  
    b.indices  # [26, 37]
    tf.shape(b.values)  # [2, 10]
    ```
  
    Args:
      a: An `IndexedSlices` instance.
      mask_indices: Indices of elements to mask.
      name: A name for the operation (optional).
  
    Returns:
      The masked `IndexedSlices` instance.
    """
    with ops.name_scope(name, "sparse_mask", [a, mask_indices]) as name:
      indices = a.indices
      out_indices, to_gather = setdiff1d(indices, mask_indices)
      out_values = gather(a.values, to_gather, name=name)
      return ops.IndexedSlices(out_values, out_indices, a.dense_shape)
  
  
  @tf_export("unique")
  def unique(x, out_idx=dtypes.int32, name=None):
    # TODO(yongtang): switch to v2 once API deprecation
    # period (3 weeks) pass.
    # TODO(yongtang): The documentation should also
    # be updated when switch  to v2.
    return gen_array_ops.unique(x, out_idx, name)
  
  
  unique.__doc__ = gen_array_ops.unique.__doc__
  
  
  @tf_export("unique_with_counts")
  def unique_with_counts(x, out_idx=dtypes.int32, name=None):
    # TODO(yongtang): switch to v2 once API deprecation
    # period (3 weeks) pass.
    # TODO(yongtang): The documentation should also
    # be updated when switch  to v2.
    return gen_array_ops.unique_with_counts(x, out_idx, name)
  
  
  unique_with_counts.__doc__ = gen_array_ops.unique_with_counts.__doc__
  
  
  @tf_export("split")
  def split(value, num_or_size_splits, axis=0, num=None, name="split"):
    """Splits a tensor into sub tensors.
  
    If `num_or_size_splits` is an integer type, then `value` is split
    along dimension `axis` into `num_split` smaller tensors.
    Requires that `num_split` evenly divides `value.shape[axis]`.
  
    If `num_or_size_splits` is not an integer type, it is presumed to be a Tensor
    `size_splits`, then splits `value` into `len(size_splits)` pieces. The shape
    of the `i`-th piece has the same size as the `value` except along dimension
    `axis` where the size is `size_splits[i]`.
  
    For example:
  
    ```python
    # 'value' is a tensor with shape [5, 30]
    # Split 'value' into 3 tensors with sizes [4, 15, 11] along dimension 1
    split0, split1, split2 = tf.split(value, [4, 15, 11], 1)
    tf.shape(split0)  # [5, 4]
    tf.shape(split1)  # [5, 15]
    tf.shape(split2)  # [5, 11]
    # Split 'value' into 3 tensors along dimension 1
    split0, split1, split2 = tf.split(value, num_or_size_splits=3, axis=1)
    tf.shape(split0)  # [5, 10]
    ```
  
    Args:
      value: The `Tensor` to split.
      num_or_size_splits: Either a 0-D integer `Tensor` indicating the number of
        splits along split_dim or a 1-D integer `Tensor` containing
        the sizes of each output tensor along split_dim. If a scalar then it must
        evenly divide `value.shape[axis]`; otherwise the sum of sizes along the
        split dimension must match that of the `value`.
      axis: A 0-D `int32` `Tensor`. The dimension along which to split.
        Must be in the range `[-rank(value), rank(value))`. Defaults to 0.
      num: Optional, used to specify the number of outputs when it cannot be
        inferred from the shape of `size_splits`.
      name: A name for the operation (optional).
  
    Returns:
      if `num_or_size_splits` is a scalar returns `num_or_size_splits` `Tensor`
      objects; if `num_or_size_splits` is a 1-D Tensor returns
      `num_or_size_splits.get_shape[0]` `Tensor` objects resulting from splitting
      `value`.
  
    Raises:
      ValueError: If `num` is unspecified and cannot be inferred.
    """
    size_splits = ops.convert_to_tensor(num_or_size_splits)
    if size_splits._rank() == 0 and size_splits.dtype.is_integer:
      return gen_array_ops.split(
          axis=axis, num_split=num_or_size_splits, value=value, name=name)
  
    if num is None:
      size_splits_shape = size_splits._shape_tuple()
      if size_splits_shape:
        num = size_splits_shape[0]
      if num is None:
        raise ValueError("Cannot infer num from shape %s" % num_or_size_splits)
  
    return gen_array_ops.split_v(
        value=value, size_splits=size_splits, axis=axis, num_split=num, name=name)
  
  
  @tf_export("transpose")
  def transpose(a, perm=None, name="transpose", conjugate=False):
    """Transposes `a`. Permutes the dimensions according to `perm`.
  
    The returned tensor's dimension i will correspond to the input dimension
    `perm[i]`. If `perm` is not given, it is set to (n-1...0), where n is
    the rank of the input tensor. Hence by default, this operation performs a
    regular matrix transpose on 2-D input Tensors. If conjugate is True and
    `a.dtype` is either `complex64` or `complex128` then the values of `a`
    are conjugated and transposed.
  
    @compatibility(numpy)
    In `numpy` transposes are memory-efficient constant time operations as they
    simply return a new view of the same data with adjusted `strides`.
  
    TensorFlow does not support strides, so `transpose` returns a new tensor with
    the items permuted.
    @end_compatibility
  
    For example:
  
    ```python
    x = tf.constant([[1, 2, 3], [4, 5, 6]])
    tf.transpose(x)  # [[1, 4]
                     #  [2, 5]
                     #  [3, 6]]
  
    # Equivalently
    tf.transpose(x, perm=[1, 0])  # [[1, 4]
                                  #  [2, 5]
                                  #  [3, 6]]
  
    # If x is complex, setting conjugate=True gives the conjugate transpose
    x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j],
                     [4 + 4j, 5 + 5j, 6 + 6j]])
    tf.transpose(x, conjugate=True)  # [[1 - 1j, 4 - 4j],
                                     #  [2 - 2j, 5 - 5j],
                                     #  [3 - 3j, 6 - 6j]]
  
    # 'perm' is more useful for n-dimensional tensors, for n > 2
    x = tf.constant([[[ 1,  2,  3],
                      [ 4,  5,  6]],
                     [[ 7,  8,  9],
                      [10, 11, 12]]])
  
    # Take the transpose of the matrices in dimension-0
    # (this common operation has a shorthand `linalg.transpose`)
    tf.transpose(x, perm=[0, 2, 1])  # [[[1,  4],
                                     #   [2,  5],
                                     #   [3,  6]],
                                     #  [[7, 10],
                                     #   [8, 11],
                                     #   [9, 12]]]
    ```
  
    Args:
      a: A `Tensor`.
      perm: A permutation of the dimensions of `a`.
      name: A name for the operation (optional).
      conjugate: Optional bool. Setting it to `True` is mathematically equivalent
        to tf.conj(tf.transpose(input)).
  
    Returns:
      A transposed `Tensor`.
    """
    with ops.name_scope(name, "transpose", [a]) as name:
      transpose_fn = (
          gen_array_ops.conjugate_transpose
          if (conjugate and a.dtype.is_complex) else gen_array_ops.transpose)
      if perm is None:
        rank = gen_array_ops.rank(a)
        perm = (rank - 1) - gen_math_ops._range(0, rank, 1)
        ret = transpose_fn(a, perm, name=name)
        # NOTE(mrry): Setting the shape explicitly because
        #   reverse is not handled by the shape function.
        if not context.executing_eagerly():
          input_shape = ret.op.inputs[0].get_shape().dims
          if input_shape is not None:
            ret.set_shape(input_shape[::-1])
      else:
        ret = transpose_fn(a, perm, name=name)
      return ret
  
  
  # pylint: disable=invalid-name
  @tf_export("linalg.transpose", "matrix_transpose")
  @deprecation.deprecated_endpoints("matrix_transpose")
  def matrix_transpose(a, name="matrix_transpose", conjugate=False):
    """Transposes last two dimensions of tensor `a`.
  
    For example:
  
    ```python
    x = tf.constant([[1, 2, 3], [4, 5, 6]])
    tf.linalg.transpose(x)  # [[1, 4],
                            #  [2, 5],
                            #  [3, 6]]
  
    x = tf.constant([[1 + 1j, 2 + 2j, 3 + 3j],
                     [4 + 4j, 5 + 5j, 6 + 6j]])
    tf.linalg.transpose(x, conjugate=True)  # [[1 - 1j, 4 - 4j],
                                            #  [2 - 2j, 5 - 5j],
                                            #  [3 - 3j, 6 - 6j]]
  
    # Matrix with two batch dimensions.
    # x.shape is [1, 2, 3, 4]
    # tf.linalg.transpose(x) is shape [1, 2, 4, 3]
    ```
  
    Note that `tf.matmul` provides kwargs allowing for transpose of arguments.
    This is done with minimal cost, and is preferable to using this function. E.g.
  
    ```python
    # Good!  Transpose is taken at minimal additional cost.
    tf.matmul(matrix, b, transpose_b=True)
  
    # Inefficient!
    tf.matmul(matrix, tf.linalg.transpose(b))
    ```
  
    @compatibility(numpy)
    In `numpy` transposes are memory-efficient constant time operations as they
    simply return a new view of the same data with adjusted `strides`.
  
    TensorFlow does not support strides, `linalg.transposes` return a new tensor
    with the items permuted.
    @end_compatibility
  
    Args:
      a: A `Tensor` with `rank >= 2`.
      name: A name for the operation (optional).
      conjugate: Optional bool. Setting it to `True` is mathematically equivalent
        to tf.conj(tf.linalg.transpose(input)).
  
    Returns:
      A transposed batch matrix `Tensor`.
  
    Raises:
      ValueError:  If `a` is determined statically to have `rank < 2`.
    """
    with ops.name_scope(name, values=[a]):
      a = ops.convert_to_tensor(a, name="a")
  
      # If we know the number of dimensions (statically), we can do two things:
      # 1. Check that `a` is a (batch) matrix.
      # 2. Use a python list for perm.  This preserves static shape information
      #    and avoids extra computations.
      a_shape = a.get_shape()
      ndims = a_shape.ndims
      if ndims is not None:
        if ndims < 2:
          raise ValueError(
              "Argument 'a' should be a (batch) matrix, with rank >= 2.  Found: "
              "%s" % a_shape)
        perm = list(range(ndims - 2)) + [ndims - 1] + [ndims - 2]
      else:
        a_rank = rank(a)
        perm = concat((gen_math_ops._range(0, a_rank - 2, 1),
                       [a_rank - 1, a_rank - 2]), 0)
  
      return transpose(a, perm=perm, conjugate=conjugate)
  
  
  # pylint: enable=invalid-name
  
  
  def _constant_if_small(value, shape, dtype, name):
    try:
      if np.prod(shape) < 1000:
        return constant(value, shape=shape, dtype=dtype, name=name)
    except TypeError:
      # Happens when shape is a Tensor, list with Tensor elements, etc.
      pass
    return None
  
  
  @tf_export("zeros")
  def zeros(shape, dtype=dtypes.float32, name=None):
    """Creates a tensor with all elements set to zero.
  
    This operation returns a tensor of type `dtype` with shape `shape` and
    all elements set to zero.
  
    For example:
  
    ```python
    tf.zeros([3, 4], tf.int32)  # [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
    ```
  
    Args:
      shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type
        `int32`.
      dtype: The type of an element in the resulting `Tensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with all elements set to zero.
    """
    dtype = dtypes.as_dtype(dtype).base_dtype
    with ops.name_scope(name, "zeros", [shape]) as name:
      if dtype == dtypes.bool:
        zero = False
      elif dtype == dtypes.string:
        zero = ""
      else:
        zero = 0
  
      if not isinstance(shape, ops.Tensor):
        try:
          # Create a constant if it won't be very big. Otherwise create a fill op
          # to prevent serialized GraphDefs from becoming too large.
          output = _constant_if_small(zero, shape, dtype, name)
          if output is not None:
            return output
  
          # Go through tensor shapes to get int64-if-needed semantics
          shape = constant_op._tensor_shape_tensor_conversion_function(
              tensor_shape.TensorShape(shape))
        except (TypeError, ValueError):
          # Happens when shape is a list with tensor elements
          shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
      if not shape._shape_tuple():
        shape = reshape(shape, [-1])  # Ensure it's a vector
      output = fill(shape, constant(zero, dtype=dtype), name=name)
    assert output.dtype.base_dtype == dtype
    return output
  
  
  @tf_export("zeros_like")
  def zeros_like(tensor, dtype=None, name=None, optimize=True):
    """Creates a tensor with all elements set to zero.
  
    Given a single tensor (`tensor`), this operation returns a tensor of the
    same type and shape as `tensor` with all elements set to zero. Optionally,
    you can use `dtype` to specify a new type for the returned tensor.
  
    For example:
  
    ```python
    tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
    tf.zeros_like(tensor)  # [[0, 0, 0], [0, 0, 0]]
    ```
  
    Args:
      tensor: A `Tensor`.
      dtype: A type for the returned `Tensor`. Must be `float16`, `float32`,
        `float64`, `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`,
        `complex64`, `complex128`, `bool` or `string`.
      name: A name for the operation (optional).
      optimize: if true, attempt to statically determine the shape of 'tensor'
      and encode it as a constant.
  
    Returns:
      A `Tensor` with all elements set to zero.
    """
    with ops.name_scope(name, "zeros_like", [tensor]) as name:
      tensor = ops.convert_to_tensor(tensor, name="tensor")
  
      if context.executing_eagerly():
        if dtype is not None and dtype != tensor.dtype:
          return zeros(
              shape_internal(tensor, optimize=optimize), dtype=dtype, name=name)
        with ops.device(tensor.device):
          return gen_array_ops.zeros_like(tensor, name=name)
  
      # For now, variant types must be created via zeros_like; as we need to
      # pass the input variant object to the proper zeros callback.
  
      if (optimize and tensor.shape.is_fully_defined() and
          tensor.dtype != dtypes.variant):
        # We can produce a zeros tensor independent of the value of 'tensor',
        # since the shape is known statically.
        return zeros(tensor.shape, dtype=dtype or tensor.dtype, name=name)
  
      if dtype is not None and dtype != tensor.dtype and dtype != dtypes.variant:
        return zeros(
            shape_internal(tensor, optimize=optimize), dtype=dtype, name=name)
      else:
        return gen_array_ops.zeros_like(tensor, name=name)
  
  
  @tf_export("ones_like")
  def ones_like(tensor, dtype=None, name=None, optimize=True):
    """Creates a tensor with all elements set to 1.
  
    Given a single tensor (`tensor`), this operation returns a tensor of the same
    type and shape as `tensor` with all elements set to 1. Optionally, you can
    specify a new type (`dtype`) for the returned tensor.
  
    For example:
  
    ```python
    tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
    tf.ones_like(tensor)  # [[1, 1, 1], [1, 1, 1]]
    ```
  
    Args:
      tensor: A `Tensor`.
      dtype: A type for the returned `Tensor`. Must be `float32`, `float64`,
        `int8`, `uint8`, `int16`, `uint16`, `int32`, `int64`,
        `complex64`, `complex128` or `bool`.
      name: A name for the operation (optional).
      optimize: if true, attempt to statically determine the shape of 'tensor'
      and encode it as a constant.
  
    Returns:
      A `Tensor` with all elements set to 1.
    """
    with ops.name_scope(name, "ones_like", [tensor]) as name:
      tensor = ops.convert_to_tensor(tensor, name="tensor")
      ones_shape = shape_internal(tensor, optimize=optimize)
      if dtype is None:
        dtype = tensor.dtype
      ret = ones(ones_shape, dtype=dtype, name=name)
      if not context.executing_eagerly():
        ret.set_shape(tensor.get_shape())
      return ret
  
  
  @tf_export("ones")
  def ones(shape, dtype=dtypes.float32, name=None):
    """Creates a tensor with all elements set to 1.
  
    This operation returns a tensor of type `dtype` with shape `shape` and all
    elements set to 1.
  
    For example:
  
    ```python
    tf.ones([2, 3], tf.int32)  # [[1, 1, 1], [1, 1, 1]]
    ```
  
    Args:
      shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type
        `int32`.
      dtype: The type of an element in the resulting `Tensor`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` with all elements set to 1.
    """
    dtype = dtypes.as_dtype(dtype).base_dtype
    with ops.name_scope(name, "ones", [shape]) as name:
      one = True if dtype == dtypes.bool else 1
      if not isinstance(shape, ops.Tensor):
        try:
          # Create a constant if it won't be very big. Otherwise create a fill op
          # to prevent serialized GraphDefs from becoming too large.
          output = _constant_if_small(one, shape, dtype, name)
          if output is not None:
            return output
  
          # Go through tensor shapes to get int64-if-needed semantics
          shape = constant_op._tensor_shape_tensor_conversion_function(
              tensor_shape.TensorShape(shape))
        except (TypeError, ValueError):
          # Happens when shape is a list with tensor elements
          shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
      if not shape._shape_tuple():
        shape = reshape(shape, [-1])  # Ensure it's a vector
      output = fill(shape, constant(one, dtype=dtype), name=name)
    assert output.dtype.base_dtype == dtype
    return output
  
  
  @tf_export("placeholder")
  def placeholder(dtype, shape=None, name=None):
    """Inserts a placeholder for a tensor that will be always fed.
  
    **Important**: This tensor will produce an error if evaluated. Its value must
    be fed using the `feed_dict` optional argument to `Session.run()`,
    `Tensor.eval()`, or `Operation.run()`.
  
    For example:
  
    ```python
    x = tf.placeholder(tf.float32, shape=(1024, 1024))
    y = tf.matmul(x, x)
  
    with tf.Session() as sess:
      print(sess.run(y))  # ERROR: will fail because x was not fed.
  
      rand_array = np.random.rand(1024, 1024)
      print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.
    ```
  
    @compatibility(eager)
    Placeholders are not compatible with eager execution.
    @end_compatibility
  
    Args:
      dtype: The type of elements in the tensor to be fed.
      shape: The shape of the tensor to be fed (optional). If the shape is not
        specified, you can feed a tensor of any shape.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor` that may be used as a handle for feeding a value, but not
      evaluated directly.
  
    Raises:
      RuntimeError: if eager execution is enabled
    """
    if context.executing_eagerly():
      raise RuntimeError("tf.placeholder() is not compatible with "
                         "eager execution.")
  
    return gen_array_ops.placeholder(dtype=dtype, shape=shape, name=name)
  
  
  # pylint: disable=redefined-outer-name
  def _normalize_sparse_shape(shape, name):
    """Returns a tuple of (Tensor or None, rank or None)."""
    if shape is None:
      return (None, None)
    rank = shape.get_shape()[0] if isinstance(shape, ops.Tensor) else len(shape)
    if not isinstance(shape, ops.Tensor) and None in shape:
      return (None, rank)
    return (ops.convert_to_tensor(shape, dtype=dtypes.int64, name=name), rank)
  
  
  @tf_export("sparse.placeholder", "sparse_placeholder")
  @deprecation.deprecated_endpoints("sparse_placeholder")
  def sparse_placeholder(dtype, shape=None, name=None):
    """Inserts a placeholder for a sparse tensor that will be always fed.
  
    **Important**: This sparse tensor will produce an error if evaluated.
    Its value must be fed using the `feed_dict` optional argument to
    `Session.run()`, `Tensor.eval()`, or `Operation.run()`.
  
    For example:
  
    ```python
    x = tf.sparse.placeholder(tf.float32)
    y = tf.sparse.reduce_sum(x)
  
    with tf.Session() as sess:
      print(sess.run(y))  # ERROR: will fail because x was not fed.
  
      indices = np.array([[3, 2, 0], [4, 5, 1]], dtype=np.int64)
      values = np.array([1.0, 2.0], dtype=np.float32)
      shape = np.array([7, 9, 2], dtype=np.int64)
      print(sess.run(y, feed_dict={
        x: tf.SparseTensorValue(indices, values, shape)}))  # Will succeed.
      print(sess.run(y, feed_dict={
        x: (indices, values, shape)}))  # Will succeed.
  
      sp = tf.SparseTensor(indices=indices, values=values, dense_shape=shape)
      sp_value = sp.eval(session=sess)
      print(sess.run(y, feed_dict={x: sp_value}))  # Will succeed.
    ```
  
    @compatibility{eager} Placeholders are not compatible with eager execution.
  
    Args:
      dtype: The type of `values` elements in the tensor to be fed.
      shape: The shape of the tensor to be fed (optional). If the shape is not
        specified, you can feed a sparse tensor of any shape.
      name: A name for prefixing the operations (optional).
  
    Returns:
      A `SparseTensor` that may be used as a handle for feeding a value, but not
      evaluated directly.
  
    Raises:
      RuntimeError: if eager execution is enabled
    """
    if context.executing_eagerly():
      raise RuntimeError("tf.placeholder() is not compatible with "
                         "eager execution.")
  
    shape_name = (name + "/shape") if name is not None else None
    shape, rank = _normalize_sparse_shape(shape, shape_name)
    if shape is None:
      shape = placeholder(dtypes.int64, shape=[rank], name=shape_name)
    return sparse_tensor.SparseTensor(
        values=placeholder(
            dtype,
            shape=[None],
            name=(name + "/values") if name is not None else None),
        indices=placeholder(
            dtypes.int64, shape=[None, rank],
            name=(name + "/indices") if name is not None else None),
        dense_shape=shape)
  
  
  # pylint: enable=redefined-outer-name
  
  
  @tf_export("pad")
  def pad(tensor, paddings, mode="CONSTANT", name=None, constant_values=0):  # pylint: disable=invalid-name
    """Pads a tensor.
  
    This operation pads a `tensor` according to the `paddings` you specify.
    `paddings` is an integer tensor with shape `[n, 2]`, where n is the rank of
    `tensor`. For each dimension D of `input`, `paddings[D, 0]` indicates how
    many values to add before the contents of `tensor` in that dimension, and
    `paddings[D, 1]` indicates how many values to add after the contents of
    `tensor` in that dimension. If `mode` is "REFLECT" then both `paddings[D, 0]`
    and `paddings[D, 1]` must be no greater than `tensor.dim_size(D) - 1`. If
    `mode` is "SYMMETRIC" then both `paddings[D, 0]` and `paddings[D, 1]` must be
    no greater than `tensor.dim_size(D)`.
  
    The padded size of each dimension D of the output is:
  
    `paddings[D, 0] + tensor.dim_size(D) + paddings[D, 1]`
  
    For example:
  
    ```python
    t = tf.constant([[1, 2, 3], [4, 5, 6]])
    paddings = tf.constant([[1, 1,], [2, 2]])
    # 'constant_values' is 0.
    # rank of 't' is 2.
    tf.pad(t, paddings, "CONSTANT")  # [[0, 0, 0, 0, 0, 0, 0],
                                     #  [0, 0, 1, 2, 3, 0, 0],
                                     #  [0, 0, 4, 5, 6, 0, 0],
                                     #  [0, 0, 0, 0, 0, 0, 0]]
  
    tf.pad(t, paddings, "REFLECT")  # [[6, 5, 4, 5, 6, 5, 4],
                                    #  [3, 2, 1, 2, 3, 2, 1],
                                    #  [6, 5, 4, 5, 6, 5, 4],
                                    #  [3, 2, 1, 2, 3, 2, 1]]
  
    tf.pad(t, paddings, "SYMMETRIC")  # [[2, 1, 1, 2, 3, 3, 2],
                                      #  [2, 1, 1, 2, 3, 3, 2],
                                      #  [5, 4, 4, 5, 6, 6, 5],
                                      #  [5, 4, 4, 5, 6, 6, 5]]
    ```
  
    Args:
      tensor: A `Tensor`.
      paddings: A `Tensor` of type `int32`.
      mode: One of "CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive)
      name: A name for the operation (optional).
      constant_values: In "CONSTANT" mode, the scalar pad value to use. Must be
        same type as `tensor`.
  
    Returns:
      A `Tensor`. Has the same type as `tensor`.
  
    Raises:
      ValueError: When mode is not one of "CONSTANT", "REFLECT", or "SYMMETRIC".
    """
  
    # Convert lower/mixed case to upper for NumPy compatibility
    # NumPy uses all lower-case modes.
    mode = mode.upper()
    if mode == "CONSTANT":
      # TODO(rjryan): Once the forward compatibility period (3 weeks) have passed
      # remove the "Pad" fallback here.
      if constant_values != 0:
        result = gen_array_ops.pad_v2(
            tensor, paddings, constant_values, name=name)
      else:
        result = gen_array_ops.pad(tensor, paddings, name=name)
    elif mode == "REFLECT":
      result = gen_array_ops.mirror_pad(
          tensor, paddings, mode="REFLECT", name=name)
    elif mode == "SYMMETRIC":
      result = gen_array_ops.mirror_pad(
          tensor, paddings, mode="SYMMETRIC", name=name)
    else:
      raise ValueError("Unknown padding mode: %s" % mode)
  
    # Restore shape information where possible.
    if not context.executing_eagerly():
      paddings_constant = tensor_util.constant_value(
          result.op.inputs[1], partial=True)
      input_shape = result.op.inputs[0].shape
      if (input_shape.ndims is not None and not result.shape.is_fully_defined()
          and paddings_constant is not None):
        new_shape = []
        for padding, dim in zip(paddings_constant, input_shape.as_list()):
          if padding is None or dim is None or any((x is None for x in padding)):
            new_shape.append(None)
          else:
            new_shape.append(sum(padding) + dim)
        result.set_shape(new_shape)
  
    return result
  
  
  @tf_export("meshgrid")
  def meshgrid(*args, **kwargs):
    """Broadcasts parameters for evaluation on an N-D grid.
  
    Given N one-dimensional coordinate arrays `*args`, returns a list `outputs`
    of N-D coordinate arrays for evaluating expressions on an N-D grid.
  
    Notes:
  
    `meshgrid` supports cartesian ('xy') and matrix ('ij') indexing conventions.
    When the `indexing` argument is set to 'xy' (the default), the broadcasting
    instructions for the first two dimensions are swapped.
  
    Examples:
  
    Calling `X, Y = meshgrid(x, y)` with the tensors
  
    ```python
    x = [1, 2, 3]
    y = [4, 5, 6]
    X, Y = tf.meshgrid(x, y)
    # X = [[1, 2, 3],
    #      [1, 2, 3],
    #      [1, 2, 3]]
    # Y = [[4, 4, 4],
    #      [5, 5, 5],
    #      [6, 6, 6]]
    ```
  
    Args:
      *args: `Tensor`s with rank 1.
      **kwargs:
        - indexing: Either 'xy' or 'ij' (optional, default: 'xy').
        - name: A name for the operation (optional).
  
    Returns:
      outputs: A list of N `Tensor`s with rank N.
  
    Raises:
      TypeError: When no keyword arguments (kwargs) are passed.
      ValueError: When indexing keyword argument is not one of `xy` or `ij`.
    """
  
    indexing = kwargs.pop("indexing", "xy")
    name = kwargs.pop("name", "meshgrid")
    if kwargs:
      key = list(kwargs.keys())[0]
      raise TypeError("'{}' is an invalid keyword argument "
                      "for this function".format(key))
  
    if indexing not in ("xy", "ij"):
      raise ValueError("indexing parameter must be either 'xy' or 'ij'")
  
    with ops.name_scope(name, "meshgrid", args) as name:
      ndim = len(args)
      s0 = (1,) * ndim
  
      # Prepare reshape by inserting dimensions with size 1 where needed
      output = []
      for i, x in enumerate(args):
        output.append(reshape(stack(x), (s0[:i] + (-1,) + s0[i + 1::])))
      # Create parameters for broadcasting each tensor to the full size
      shapes = [size(x) for x in args]
  
      output_dtype = ops.convert_to_tensor(args[0]).dtype.base_dtype
  
      if indexing == "xy" and ndim > 1:
        output[0] = reshape(output[0], (1, -1) + (1,) * (ndim - 2))
        output[1] = reshape(output[1], (-1, 1) + (1,) * (ndim - 2))
        shapes[0], shapes[1] = shapes[1], shapes[0]
  
      # TODO(nolivia): improve performance with a broadcast
      mult_fact = ones(shapes, output_dtype)
      return [x * mult_fact for x in output]
  
  
  NEW_AXIS = -1
  SHRINK_AXIS = -2
  
  
  # PEP-8 naming
  # pylint: disable=invalid-name,redefined-outer-name
  def _compute_size_of_strided_dim(shrink, spec, size):
    """Computes the size of a single strided slice dimension."""
  
    unknown = None  # Document what None means here.
    use_full_range = None  # Document other use of None.
    # if this is a shrink axis (i.e. a non-range index)
    # it either will produce an error or return 1
    if shrink:
      return 1
    if size is unknown or size.value is unknown:
      return unknown
    size = size.value
    stride = spec.step
    if stride is not unknown:
      if stride == 0:
        return unknown
      stride = spec.step
      valid_range = [0, size] if stride > 0 else [-1, size - 1]
  
      # PEP-8 naming
      # pylint: disable=invalid-name
      def canonical(x, c):
        if x is use_full_range:
          return valid_range[c] if stride > 0 else valid_range[(c + 1) & 1]
        else:
          x_fwd = size + x if x < 0 else x  # make negative indices positive
          return max(valid_range[0], min(valid_range[1], x_fwd))
  
      begin = canonical(spec.start, 0)
      end = canonical(spec.stop, 1)
      interval_length = end - begin
      if interval_length == 0 or ((interval_length < 0) != (stride < 0)):
        return 0
      else:
        remainder = 1 if interval_length % stride != 0 else 0
        return interval_length // stride + remainder
    else:
      return unknown  # unknown because stride is unknown
  
  
  def _TileGradShape(op):
    """Shape function for the TileGrad op."""
    multiples_shape = op.inputs[1].get_shape().with_rank(1)
    input_shape = op.inputs[0].get_shape().with_rank(multiples_shape[0])
    # NOTE(mrry): Represent `multiples` as a `TensorShape` because (i)
    # it is a vector of non-negative integers, and (ii) doing so allows
    # us to handle partially-known multiples.
    multiples = tensor_util.constant_value_as_shape(op.inputs[1]).with_rank(
        input_shape.ndims)
    if multiples.ndims is None:
      return [tensor_shape.unknown_shape()]
    else:
      output_dims = []
      for dim, multiple in zip(input_shape.dims, multiples.dims):
        output_dims.append(dim // multiple)
      return [tensor_shape.TensorShape(output_dims)]
  
  
  @tf_export("edit_distance")
  def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"):
    """Computes the Levenshtein distance between sequences.
  
    This operation takes variable-length sequences (`hypothesis` and `truth`),
    each provided as a `SparseTensor`, and computes the Levenshtein distance.
    You can normalize the edit distance by length of `truth` by setting
    `normalize` to true.
  
    For example, given the following input:
  
    ```python
    # 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
    #   (0,0) = ["a"]
    #   (1,0) = ["b"]
    hypothesis = tf.SparseTensor(
        [[0, 0, 0],
         [1, 0, 0]],
        ["a", "b"],
        (2, 1, 1))
  
    # 'truth' is a tensor of shape `[2, 2]` with variable-length values:
    #   (0,0) = []
    #   (0,1) = ["a"]
    #   (1,0) = ["b", "c"]
    #   (1,1) = ["a"]
    truth = tf.SparseTensor(
        [[0, 1, 0],
         [1, 0, 0],
         [1, 0, 1],
         [1, 1, 0]],
        ["a", "b", "c", "a"],
        (2, 2, 2))
  
    normalize = True
    ```
  
    This operation would return the following:
  
    ```python
    # 'output' is a tensor of shape `[2, 2]` with edit distances normalized
    # by 'truth' lengths.
    output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
               [0.5, 1.0]]  # (1,0): addition, (1,1): no hypothesis
    ```
  
    Args:
      hypothesis: A `SparseTensor` containing hypothesis sequences.
      truth: A `SparseTensor` containing truth sequences.
      normalize: A `bool`. If `True`, normalizes the Levenshtein distance by
        length of `truth.`
      name: A name for the operation (optional).
  
    Returns:
      A dense `Tensor` with rank `R - 1`, where R is the rank of the
      `SparseTensor` inputs `hypothesis` and `truth`.
  
    Raises:
      TypeError: If either `hypothesis` or `truth` are not a `SparseTensor`.
    """
    if not isinstance(hypothesis, (sparse_tensor.SparseTensor,
                                   sparse_tensor.SparseTensorValue)):
      raise TypeError("Hypothesis must be a SparseTensor.")
    if not isinstance(truth, (sparse_tensor.SparseTensor,
                              sparse_tensor.SparseTensorValue)):
      raise TypeError("Truth must be a SparseTensor.")
  
    return gen_array_ops.edit_distance(
        hypothesis.indices,
        hypothesis.values,
        hypothesis.dense_shape,
        truth.indices,
        truth.values,
        truth.dense_shape,
        normalize=normalize,
        name=name)
  
  
  @ops.RegisterGradient("FakeQuantWithMinMaxArgs")
  def _FakeQuantWithMinMaxArgsGradient(op, grad):
    """Gradient for FakeQuantWithMinMaxArgs op."""
    return fake_quant_with_min_max_args_gradient(
        grad,
        op.inputs[0],
        min=op.get_attr("min"),
        max=op.get_attr("max"),
        num_bits=op.get_attr("num_bits"),
        narrow_range=op.get_attr("narrow_range"))
  
  
  @ops.RegisterGradient("FakeQuantWithMinMaxVars")
  def _FakeQuantWithMinMaxVarsGradient(op, grad):
    """Gradient for FakeQuantWithMinMaxVars op."""
    return fake_quant_with_min_max_vars_gradient(
        grad,
        op.inputs[0],
        op.inputs[1],
        op.inputs[2],
        num_bits=op.get_attr("num_bits"),
        narrow_range=op.get_attr("narrow_range"))
  
  
  @ops.RegisterGradient("FakeQuantWithMinMaxVarsPerChannel")
  def _FakeQuantWithMinMaxVarsPerChannelGradient(op, grad):
    """Gradient for FakeQuantWithMinMaxVarsPerChannel op."""
    return fake_quant_with_min_max_vars_per_channel_gradient(
        grad,
        op.inputs[0],
        op.inputs[1],
        op.inputs[2],
        num_bits=op.get_attr("num_bits"),
        narrow_range=op.get_attr("narrow_range"))
  
  
  @tf_export("required_space_to_batch_paddings")
  def required_space_to_batch_paddings(input_shape,
                                       block_shape,
                                       base_paddings=None,
                                       name=None):
    """Calculate padding required to make block_shape divide input_shape.
  
    This function can be used to calculate a suitable paddings argument for use
    with space_to_batch_nd and batch_to_space_nd.
  
    Args:
      input_shape: int32 Tensor of shape [N].
      block_shape: int32 Tensor of shape [N].
      base_paddings: Optional int32 Tensor of shape [N, 2].  Specifies the minimum
        amount of padding to use.  All elements must be >= 0.  If not specified,
        defaults to 0.
      name: string.  Optional name prefix.
  
    Returns:
      (paddings, crops), where:
  
      `paddings` and `crops` are int32 Tensors of rank 2 and shape [N, 2]
      satisfying:
  
          paddings[i, 0] = base_paddings[i, 0].
          0 <= paddings[i, 1] - base_paddings[i, 1] < block_shape[i]
          (input_shape[i] + paddings[i, 0] + paddings[i, 1]) % block_shape[i] == 0
  
          crops[i, 0] = 0
          crops[i, 1] = paddings[i, 1] - base_paddings[i, 1]
  
    Raises: ValueError if called with incompatible shapes.
    """
    with ops.name_scope(name, "required_space_to_batch_paddings",
                        [input_shape, block_shape]):
      input_shape = ops.convert_to_tensor(
          input_shape, dtype=dtypes.int32, name="input_shape")
      block_shape = ops.convert_to_tensor(
          block_shape, dtype=dtypes.int32, name="block_shape")
  
      block_shape.get_shape().assert_is_fully_defined()
      block_shape.get_shape().assert_has_rank(1)
      num_block_dims = block_shape.get_shape()[0].value
      if num_block_dims == 0:
        return zeros([0, 2], dtypes.int32), zeros([0, 2], dtypes.int32)
  
      input_shape.get_shape().assert_is_compatible_with([num_block_dims])
  
      if base_paddings is not None:
        base_paddings = ops.convert_to_tensor(
            base_paddings, dtype=dtypes.int32, name="base_paddings")
        base_paddings.get_shape().assert_is_compatible_with([num_block_dims, 2])
      else:
        base_paddings = zeros([num_block_dims, 2], dtypes.int32)
  
      const_block_shape = tensor_util.constant_value(block_shape)
      const_input_shape = tensor_util.constant_value(input_shape)
      const_base_paddings = tensor_util.constant_value(base_paddings)
      if (const_block_shape is not None and const_input_shape is not None and
          const_base_paddings is not None):
        block_shape = const_block_shape
        input_shape = const_input_shape
        base_paddings = const_base_paddings
  
      # Use same expression for both constant and non-constant case.
      pad_start = base_paddings[:, 0]
      orig_pad_end = base_paddings[:, 1]
      full_input_shape = input_shape + pad_start + orig_pad_end
      pad_end_extra = (block_shape - full_input_shape % block_shape) % block_shape
      pad_end = orig_pad_end + pad_end_extra
  
      result_paddings = stack(
          [[pad_start[i], pad_end[i]] for i in range(num_block_dims)],
          name="paddings")
      result_crops = stack(
          [[0, pad_end_extra[i]] for i in range(num_block_dims)], name="crops")
      return result_paddings, result_crops
  
  
  @tf_export("nn.space_to_batch", "space_to_batch")
  @deprecation.deprecated_endpoints("space_to_batch")
  def space_to_batch(input, paddings, block_size, name=None):  # pylint: disable=redefined-builtin
    result = space_to_batch_nd(
        input,
        paddings=paddings,
        block_shape=np.array([block_size, block_size], dtype=np.int64),
        name=name)
    result.set_shape(result.get_shape().with_rank(4))
    return result
  
  
  space_to_batch.__doc__ = gen_array_ops.space_to_batch.__doc__
  
  
  @tf_export("nn.space_to_depth", "space_to_depth")
  @deprecation.deprecated_endpoints("space_to_depth")
  def space_to_depth(input, block_size, name=None, data_format="NHWC"):  # pylint: disable=redefined-builtin
    return gen_array_ops.space_to_depth(input, block_size, data_format, name=name)
  
  
  space_to_depth.__doc__ = gen_array_ops.space_to_depth.__doc__
  
  
  @tf_export("nn.depth_to_space", "depth_to_space")
  @deprecation.deprecated_endpoints("depth_to_space")
  def depth_to_space(input, block_size, name=None, data_format="NHWC"):  # pylint: disable=redefined-builtin
    return gen_array_ops.depth_to_space(input, block_size, data_format, name=name)
  
  
  depth_to_space.__doc__ = gen_array_ops.depth_to_space.__doc__
  
  
  @tf_export("batch_to_space")
  def batch_to_space(input, crops, block_size, name=None):  # pylint: disable=redefined-builtin
    result = batch_to_space_nd(
        input,
        crops=crops,
        block_shape=np.array([block_size, block_size], dtype=np.int64),
        name=name)
    result.set_shape(result.get_shape().with_rank(4))
    return result
  
  
  batch_to_space.__doc__ = gen_array_ops.batch_to_space.__doc__
  
  
  @tf_export("one_hot")
  def one_hot(indices,
              depth,
              on_value=None,
              off_value=None,
              axis=None,
              dtype=None,
              name=None):
    """Returns a one-hot tensor.
  
    The locations represented by indices in `indices` take value `on_value`,
    while all other locations take value `off_value`.
  
    `on_value` and `off_value` must have matching data types. If `dtype` is also
    provided, they must be the same data type as specified by `dtype`.
  
    If `on_value` is not provided, it will default to the value `1` with type
    `dtype`
  
    If `off_value` is not provided, it will default to the value `0` with type
    `dtype`
  
    If the input `indices` is rank `N`, the output will have rank `N+1`. The
    new axis is created at dimension `axis` (default: the new axis is appended
    at the end).
  
    If `indices` is a scalar the output shape will be a vector of length `depth`
  
    If `indices` is a vector of length `features`, the output shape will be:
  
    ```
      features x depth if axis == -1
      depth x features if axis == 0
    ```
  
    If `indices` is a matrix (batch) with shape `[batch, features]`, the output
    shape will be:
  
    ```
      batch x features x depth if axis == -1
      batch x depth x features if axis == 1
      depth x batch x features if axis == 0
    ```
  
    If `dtype` is not provided, it will attempt to assume the data type of
    `on_value` or `off_value`, if one or both are passed in. If none of
    `on_value`, `off_value`, or `dtype` are provided, `dtype` will default to the
    value `tf.float32`.
  
    Note: If a non-numeric data type output is desired (`tf.string`, `tf.bool`,
    etc.), both `on_value` and `off_value` _must_ be provided to `one_hot`.
  
    For example:
  
    ```python
    indices = [0, 1, 2]
    depth = 3
    tf.one_hot(indices, depth)  # output: [3 x 3]
    # [[1., 0., 0.],
    #  [0., 1., 0.],
    #  [0., 0., 1.]]
  
    indices = [0, 2, -1, 1]
    depth = 3
    tf.one_hot(indices, depth,
               on_value=5.0, off_value=0.0,
               axis=-1)  # output: [4 x 3]
    # [[5.0, 0.0, 0.0],  # one_hot(0)
    #  [0.0, 0.0, 5.0],  # one_hot(2)
    #  [0.0, 0.0, 0.0],  # one_hot(-1)
    #  [0.0, 5.0, 0.0]]  # one_hot(1)
  
    indices = [[0, 2], [1, -1]]
    depth = 3
    tf.one_hot(indices, depth,
               on_value=1.0, off_value=0.0,
               axis=-1)  # output: [2 x 2 x 3]
    # [[[1.0, 0.0, 0.0],   # one_hot(0)
    #   [0.0, 0.0, 1.0]],  # one_hot(2)
    #  [[0.0, 1.0, 0.0],   # one_hot(1)
    #   [0.0, 0.0, 0.0]]]  # one_hot(-1)
    ```
  
    Args:
      indices: A `Tensor` of indices.
      depth: A scalar defining the depth of the one hot dimension.
      on_value: A scalar defining the value to fill in output when `indices[j]
        = i`. (default: 1)
      off_value: A scalar defining the value to fill in output when `indices[j]
        != i`. (default: 0)
      axis: The axis to fill (default: -1, a new inner-most axis).
      dtype: The data type of the output tensor.
      name: A name for the operation (optional).
  
    Returns:
      output: The one-hot tensor.
  
    Raises:
      TypeError: If dtype of either `on_value` or `off_value` don't match `dtype`
      TypeError: If dtype of `on_value` and `off_value` don't match one another
    """
    with ops.name_scope(name, "one_hot",
                        [indices, depth, on_value, off_value, axis,
                         dtype]) as name:
      on_exists = on_value is not None
      off_exists = off_value is not None
  
      on_dtype = (ops.convert_to_tensor(on_value).dtype.base_dtype if on_exists
                  else None)
      off_dtype = (ops.convert_to_tensor(off_value).dtype.base_dtype if off_exists
                   else None)
  
      if on_exists or off_exists:
        if dtype is not None:
          # Ensure provided on_value and/or off_value match dtype
          if on_exists and on_dtype != dtype:
            raise TypeError("dtype {0} of on_value does not match "
                            "dtype parameter {1}".format(on_dtype, dtype))
          if off_exists and off_dtype != dtype:
            raise TypeError("dtype {0} of off_value does not match "
                            "dtype parameter {1}".format(off_dtype, dtype))
        else:
          # dtype not provided: automatically assign it
          dtype = on_dtype if on_exists else off_dtype
      elif dtype is None:
        # None of on_value, off_value, or dtype provided. Default dtype to float32
        dtype = dtypes.float32
  
      if not on_exists:
        # on_value not provided: assign to value 1 of type dtype
        on_value = ops.convert_to_tensor(1, dtype, name="on_value")
        on_dtype = dtype
      if not off_exists:
        # off_value not provided: assign to value 0 of type dtype
        off_value = ops.convert_to_tensor(0, dtype, name="off_value")
        off_dtype = dtype
  
      if on_dtype != off_dtype:
        raise TypeError("dtype {0} of on_value does not match "
                        "dtype {1} of off_value".format(on_dtype, off_dtype))
  
      return gen_array_ops.one_hot(indices, depth, on_value, off_value, axis,
                                   name)
  
  
  def _all_dimensions(x):
    """Returns a 1D-tensor listing all dimensions in x."""
    # Fast path: avoid creating Rank and Range ops if ndims is known.
    if isinstance(x, ops.Tensor) and x.get_shape().ndims is not None:
      return constant_op.constant(
          np.arange(x.get_shape().ndims), dtype=dtypes.int32)
    if (isinstance(x, sparse_tensor.SparseTensor) and
        x.dense_shape.get_shape().is_fully_defined()):
      r = x.dense_shape.get_shape()[0].value  # sparse.dense_shape is 1-D.
      return constant_op.constant(np.arange(r), dtype=dtypes.int32)
  
    # Otherwise, we rely on `range` and `rank` to do the right thing at runtime.
    return gen_math_ops._range(0, rank(x), 1)
  
  
  @tf_export("sequence_mask")
  def sequence_mask(lengths, maxlen=None, dtype=dtypes.bool, name=None):
    """Returns a mask tensor representing the first N positions of each cell.
  
    If `lengths` has shape `[d_1, d_2, ..., d_n]` the resulting tensor `mask` has
    dtype `dtype` and shape `[d_1, d_2, ..., d_n, maxlen]`, with
  
    ```
    mask[i_1, i_2, ..., i_n, j] = (j < lengths[i_1, i_2, ..., i_n])
    ```
  
    Examples:
  
    ```python
    tf.sequence_mask([1, 3, 2], 5)  # [[True, False, False, False, False],
                                    #  [True, True, True, False, False],
                                    #  [True, True, False, False, False]]
  
    tf.sequence_mask([[1, 3],[2,0]])  # [[[True, False, False],
                                      #   [True, True, True]],
                                      #  [[True, True, False],
                                      #   [False, False, False]]]
    ```
  
    Args:
      lengths: integer tensor, all its values <= maxlen.
      maxlen: scalar integer tensor, size of last dimension of returned tensor.
        Default is the maximum value in `lengths`.
      dtype: output type of the resulting tensor.
      name: name of the op.
    Returns:
      A mask tensor of shape `lengths.shape + (maxlen,)`, cast to specified dtype.
    Raises:
      ValueError: if `maxlen` is not a scalar.
    """
    with ops.name_scope(name, "SequenceMask", [lengths, maxlen]):
      lengths = ops.convert_to_tensor(lengths)
  
      if maxlen is None:
        maxlen = gen_math_ops._max(lengths, _all_dimensions(lengths))
      else:
        maxlen = ops.convert_to_tensor(maxlen)
      if maxlen.get_shape().ndims is not None and maxlen.get_shape().ndims != 0:
        raise ValueError("maxlen must be scalar for sequence_mask")
  
      # The basic idea is to compare a range row vector of size maxlen:
      # [0, 1, 2, 3, 4]
      # to length as a matrix with 1 column: [[1], [3], [2]].
      # Because of broadcasting on both arguments this comparison results
      # in a matrix of size (len(lengths), maxlen)
      row_vector = gen_math_ops._range(
          constant(0, maxlen.dtype), maxlen, constant(1, maxlen.dtype))
      # Since maxlen >= max(lengths), it is safe to use maxlen as a cast
      # authoritative type. Whenever maxlen fits into tf.int32, so do the lengths.
      matrix = gen_math_ops.cast(expand_dims(lengths, -1), maxlen.dtype)
      result = row_vector < matrix
  
      if dtype is None or result.dtype.base_dtype == dtype.base_dtype:
        return result
      else:
        return gen_math_ops.cast(result, dtype)
  
  
  @tf_export("squeeze")
  @deprecation.deprecated_args(None, "Use the `axis` argument instead",
                               "squeeze_dims")
  def squeeze(input, axis=None, name=None, squeeze_dims=None):
    # pylint: disable=redefined-builtin
    """Removes dimensions of size 1 from the shape of a tensor.
  
    Given a tensor `input`, this operation returns a tensor of the same type with
    all dimensions of size 1 removed. If you don't want to remove all size 1
    dimensions, you can remove specific size 1 dimensions by specifying
    `axis`.
  
    For example:
  
    ```python
    # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
    tf.shape(tf.squeeze(t))  # [2, 3]
    ```
  
    Or, to remove specific size 1 dimensions:
  
    ```python
    # 't' is a tensor of shape [1, 2, 1, 3, 1, 1]
    tf.shape(tf.squeeze(t, [2, 4]))  # [1, 2, 3, 1]
    ```
  
    Args:
      input: A `Tensor`. The `input` to squeeze.
      axis: An optional list of `ints`. Defaults to `[]`.
        If specified, only squeezes the dimensions listed. The dimension
        index starts at 0. It is an error to squeeze a dimension that is not 1.
        Must be in the range `[-rank(input), rank(input))`.
      name: A name for the operation (optional).
      squeeze_dims: Deprecated keyword argument that is now axis.
  
    Returns:
      A `Tensor`. Has the same type as `input`.
      Contains the same data as `input`, but has one or more dimensions of
      size 1 removed.
  
    Raises:
      ValueError: When both `squeeze_dims` and `axis` are specified.
    """
    axis = deprecation.deprecated_argument_lookup(
        "axis", axis, "squeeze_dims", squeeze_dims)
    if np.isscalar(axis):
      axis = [axis]
    return gen_array_ops.squeeze(input, axis, name)
  
  
  @tf_export("where")
  def where(condition, x=None, y=None, name=None):
    """Return the elements, either from `x` or `y`, depending on the `condition`.
  
    If both `x` and `y` are None, then this operation returns the coordinates of
    true elements of `condition`.  The coordinates are returned in a 2-D tensor
    where the first dimension (rows) represents the number of true elements, and
    the second dimension (columns) represents the coordinates of the true
    elements. Keep in mind, the shape of the output tensor can vary depending on
    how many true values there are in input. Indices are output in row-major
    order.
  
    If both non-None, `x` and `y` must have the same shape.
    The `condition` tensor must be a scalar if `x` and `y` are scalar.
    If `x` and `y` are vectors of higher rank, then `condition` must be either 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`.
  
    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 shape and type as `x`.
      name: A name of the operation (optional)
  
    Returns:
      A `Tensor` with the same type and shape as `x`, `y` if they are non-None.
      A `Tensor` with shape `(num_true, dim_size(condition))`.
  
    Raises:
      ValueError: When exactly one of `x` or `y` is non-None.
    """
    if x is None and y is None:
      with ops.name_scope(name, "Where", [condition]) as name:
        condition = ops.convert_to_tensor(
            condition, preferred_dtype=dtypes.bool, name="condition")
        return gen_array_ops.where(condition=condition, name=name)
    elif x is not None and y is not None:
      return gen_math_ops.select(condition=condition, x=x, y=y, name=name)
    else:
      raise ValueError("x and y must both be non-None or both be None.")
  
  
  # pylint: disable=redefined-builtin
  @tf_export("reverse_sequence")
  @deprecation.deprecated_args(
      None, "seq_dim is deprecated, use seq_axis instead", "seq_dim")
  @deprecation.deprecated_args(
      None, "batch_dim is deprecated, use batch_axis instead", "batch_dim")
  def reverse_sequence(input,
                       seq_lengths,
                       seq_axis=None,
                       batch_axis=None,
                       name=None,
                       seq_dim=None,
                       batch_dim=None):
    seq_axis = deprecation.deprecated_argument_lookup("seq_axis", seq_axis,
                                                      "seq_dim", seq_dim)
    batch_axis = deprecation.deprecated_argument_lookup("batch_axis", batch_axis,
                                                        "batch_dim", batch_dim)
    return gen_array_ops.reverse_sequence(
        input=input,
        seq_lengths=seq_lengths,
        seq_dim=seq_axis,
        batch_dim=batch_axis,
        name=name)
  
  
  # pylint: enable=redefined-builtin
  
  reverse_sequence.__doc__ = deprecation.rewrite_argument_docstring(
      deprecation.rewrite_argument_docstring(
          gen_array_ops.reverse_sequence.__doc__, "batch_dim", "batch_axis"),
      "seq_dim", "seq_axis")
  
  
  @tf_export("gather")
  def gather(params, indices, validate_indices=None, name=None, axis=0):
    del validate_indices
    if axis != 0:
      # Note that we do a sparse_read here to avoid snapshotting the entire
      # resource variable and doing a gather, which can be inefficient and lead to
      # subtle race conditions. TODO(apassos) implement axis != 0 on sparse_read
      return gen_array_ops.gather_v2(params, indices, axis, name=name)
    try:
      # TODO(apassos) find a less bad way of detecting resource variables without
      # introducing a circular dependency.
      return params.sparse_read(indices, name=name)
    except AttributeError:
      return gen_array_ops.gather_v2(params, indices, axis, name=name)
  
  
  gather.__doc__ = gen_array_ops.gather_v2.__doc__
  
  
  @tf_export("batch_gather")
  def batch_gather(params, indices, name=None):
    """Gather slices from `params` according to `indices` with leading batch dims.
  
    This operation assumes that the leading dimensions of `indices` are dense,
    and the gathers on the axis corresponding to the last dimension of `indices`.
    More concretely it computes:
  
    result[i1, ..., in] = params[i1, ..., in-1, indices[i1, ..., in]]
  
    Therefore `params` should be a Tensor of shape [A1, ..., AN, B1, ..., BM],
    `indices` should be a Tensor of shape [A1, ..., AN-1, C] and `result` will be
    a Tensor of size `[A1, ..., AN-1, C, B1, ..., BM]`.
  
    In the case in which indices is a 1D tensor, this operation is equivalent to
    `tf.gather`.
  
    See also `tf.gather` and `tf.gather_nd`.
  
    Args:
      params: A Tensor. The tensor from which to gather values.
      indices: A Tensor. Must be one of the following types: int32, int64. Index
          tensor. Must be in range `[0, params.shape[axis]`, where `axis` is the
          last dimension of `indices` itself.
      name: A name for the operation (optional).
  
    Returns:
      A Tensor. Has the same type as `params`.
  
    Raises:
      ValueError: if `indices` has an unknown shape.
    """
  
    with ops.name_scope(name):
      indices = ops.convert_to_tensor(indices, name="indices")
      params = ops.convert_to_tensor(params, name="params")
      indices_shape = shape(indices)
      params_shape = shape(params)
      ndims = indices.shape.ndims
      if ndims is None:
        raise ValueError("batch_gather does not allow indices with unknown "
                         "shape.")
      batch_indices = indices
      accum_dim_value = 1
      for dim in range(ndims-1, 0, -1):
        dim_value = params_shape[dim-1]
        accum_dim_value *= params_shape[dim]
        dim_indices = gen_math_ops._range(0, dim_value, 1)
        dim_indices *= accum_dim_value
        dim_shape = stack([1] * (dim - 1) + [dim_value] + [1] * (ndims - dim),
                          axis=0)
        batch_indices += reshape(dim_indices, dim_shape)
  
      flat_indices = reshape(batch_indices, [-1])
      outer_shape = params_shape[ndims:]
      flat_inner_shape = gen_math_ops.prod(
          params_shape[:ndims], [0], False)
  
      flat_params = reshape(
          params, concat([[flat_inner_shape], outer_shape], axis=0))
      flat_result = gather(flat_params, flat_indices)
      result = reshape(flat_result, concat([indices_shape, outer_shape], axis=0))
      final_shape = indices.get_shape()[:ndims-1].merge_with(
          params.get_shape()[:ndims -1])
      final_shape = final_shape.concatenate(indices.get_shape()[ndims-1])
      final_shape = final_shape.concatenate(params.get_shape()[ndims:])
      result.set_shape(final_shape)
      return result
  
  
  # Define quantize_v2 here in order to make name the second-to-last attribute,
  # because round_mode was added later.
  @tf_export("quantize_v2")
  @deprecation.deprecated(
      "2017-10-25",
      "`tf.quantize_v2` is deprecated, please use `tf.quantization.quantize` "
      "instead.")  # pylint: disable=missing-docstring
  def quantize_v2(input,  # pylint: disable=redefined-builtin
                  min_range,
                  max_range,
                  T,
                  mode="MIN_COMBINED",
                  name=None,
                  round_mode="HALF_AWAY_FROM_ZERO"):
    return gen_array_ops.quantize_v2(input,
                                     min_range,
                                     max_range,
                                     T=T,
                                     mode=mode,
                                     name=name,
                                     round_mode=round_mode)
  
  
  quantize_v2.__doc__ = """Please use `tf.quantize` instead."""
  
  
  # We want to expose tf.quantize instead of tf.quantize_v2; we can deprecate
  # tf.quantize_v2 in next version of TensorFlow.
  @tf_export("quantization.quantize", "quantize")
  @deprecation.deprecated_endpoints("quantize")
  def quantize(input,  # pylint: disable=redefined-builtin
               min_range,
               max_range,
               T,
               mode="MIN_COMBINED",
               round_mode="HALF_AWAY_FROM_ZERO",
               name=None):
    return gen_array_ops.quantize_v2(
        input,
        min_range,
        max_range,
        T,
        mode=mode,
        round_mode=round_mode,
        name=name)
  
  
  @tf_export("searchsorted")
  def searchsorted(sorted_sequence,
                   values,
                   side="left",
                   out_type=dtypes.int32,
                   name=None):
    """Searches input tensor for values on the innermost dimension.
  
    A 2-D example:
  
    ```
      sorted_sequence = [[0, 3, 9, 9, 10],
                         [1, 2, 3, 4, 5]]
      values = [[2, 4, 9],
                [0, 2, 6]]
  
      result = searchsorted(sorted_sequence, values, side="left")
  
      result == [[1, 2, 2],
                 [0, 1, 5]]
  
      result = searchsorted(sorted_sequence, values, side="right")
  
      result == [[1, 2, 4],
                 [0, 2, 5]]
    ```
  
    Args:
      sorted_sequence: N-D `Tensor` containing a sorted sequence.
      values: N-D `Tensor` containing the search values.
      side: 'left' or 'right'; 'left' corresponds to lower_bound and 'right' to
        upper_bound.
      out_type: The output type (`int32` or `int64`).  Default is `tf.int32`.
      name: Optional name for the operation.
  
    Returns:
      An N-D `Tensor` the size of values containing the result of applying either
      lower_bound or upper_bound (depending on side) to each value.  The result
      is not a global index to the entire `Tensor`, but the index in the last
      dimension.
  
    Raises:
      ValueError: If the last dimension of `sorted_sequence >= 2^31-1` elements.
                  If the total size of values exceeds `2^31 - 1` elements.
                  If the first `N-1` dimensions of the two tensors don't match.
    """
    sequence_size = shape_internal(sorted_sequence)[-1]
    values_size = shape_internal(values)[-1]
    sorted_sequence_2d = reshape(sorted_sequence, [-1, sequence_size])
    values_2d = reshape(values, [-1, values_size])
    if side == "right":
      output = gen_array_ops.upper_bound(sorted_sequence_2d, values_2d, out_type,
                                         name)
    elif side == "left":
      output = gen_array_ops.lower_bound(sorted_sequence_2d, values_2d, out_type,
                                         name)
    else:
      raise ValueError("side must be either 'right' or 'left'.  Saw: %s." % side)
    return reshape(output, shape_internal(values))
  
  
  quantize.__doc__ = gen_array_ops.quantize_v2.__doc__
  