parameters: (x, y, name=None)
docstring: r"""Computes the power of one value to another.
  
    Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
    corresponding elements in `x` and `y`. For example:
  
    ```python
    x = tf.constant([[2, 2], [3, 3]])
    y = tf.constant([[8, 16], [2, 3]])
    tf.pow(x, y)  # [[256, 65536], [9, 27]]
    ```
  
    Args:
      x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`,
        `complex64`, or `complex128`.
      y: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`,
        `complex64`, or `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`.
docstring_summary: r"""Computes the power of one value to another.
docstring_tokens: r Computes the power of one value to another .
function: def pow(x, y, name=None):  # pylint: disable=redefined-builtin
    r"""Computes the power of one value to another.
  
    Given a tensor `x` and a tensor `y`, this operation computes \\(x^y\\) for
    corresponding elements in `x` and `y`. For example:
  
    ```python
    x = tf.constant([[2, 2], [3, 3]])
    y = tf.constant([[8, 16], [2, 3]])
    tf.pow(x, y)  # [[256, 65536], [9, 27]]
    ```
  
    Args:
      x: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`,
        `complex64`, or `complex128`.
      y: A `Tensor` of type `float16`, `float32`, `float64`, `int32`, `int64`,
        `complex64`, or `complex128`.
      name: A name for the operation (optional).
  
    Returns:
      A `Tensor`.
    """
    with ops.name_scope(name, "Pow", [x]) as name:
      return gen_math_ops._pow(x, y, name=name)
function_tokens: def pow ( x , y , name = None ) : # pylint: disable=redefined-builtin with ops . name_scope ( name , "Pow" , [ x ] ) as name : return gen_math_ops . _pow ( x , y , name = name )