parameters: (tensor, dtype=None, name=None, optimize=True)
docstring: 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`, `int16`, `int32`, `int64`, `uint8`, `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.
docstring_summary: Creates a tensor with all elements set to 1.
docstring_tokens: Creates a tensor with all elements set to 1 .
function: 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`, `int16`, `int32`, `int64`, `uint8`, `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 context.in_graph_mode():
        ret.set_shape(tensor.get_shape())
      return ret
function_tokens: def ones_like ( tensor , dtype = None , name = None , optimize = True ) : 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 context . in_graph_mode ( ) : ret . set_shape ( tensor . get_shape ( ) ) return ret