parameters: (input_, begin, size, name=None)
return_statement: return gen_array_ops._slice(input_, begin, size, name=name)
docstring: 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([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`.
docstring_summary: Extracts a slice from a tensor.
docstring_tokens: Extracts a slice from a tensor .
function: 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([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)
function_tokens: def slice ( input_ , begin , size , name = None ) : # pylint: disable=redefined-builtin return gen_array_ops . _slice ( input_ , begin , size , name = name )