.. _l-onnx-op-pow-d1-15:

Pow - 1 vs 15
=============

Next section compares an older to a newer version of the same operator 
after both definition are converted into markdown text.
Green means an addition to the newer version, red means a deletion.
Anything else is unchanged.

.. raw:: html


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    --- a/Pow1
    +++ b/Pow15
    @@ -1 +1 @@
      Pow takes input data (Tensor<T>) and exponent Tensor, and
      produces one output data (Tensor<T>) where the function f(x) = x^exponent,
      is applied to the data tensor elementwise.
    + This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [Broadcasting in ONNX](https://github.com/onnx/onnx/blob/main/docs/Broadcasting.md).
    - 
    - If necessary the right-hand-side argument will be broadcasted to match the
    - shape of left-hand-side argument. When broadcasting is specified, the second
    - tensor can either be of element size 1 (including a scalar tensor and any
    - tensor with rank equal to or smaller than the first tensor), or having its
    - shape as a contiguous subset of the first tensor's shape. The starting of the
    - mutually equal shape is specified by the argument "axis", and if it is not set,
    - suffix matching is assumed. 1-dim expansion doesn't work yet.
    - 
    - For example, the following tensor shapes are supported (with broadcast=1):
    - 
    -   shape(A) = (2, 3, 4, 5), shape(B) = (,), i.e. B is a scalar tensor
    -   shape(A) = (2, 3, 4, 5), shape(B) = (1, 1), i.e. B is an 1-element tensor
    -   shape(A) = (2, 3, 4, 5), shape(B) = (5,)
    -   shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
    -   shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
    -   shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
    - 
    - Attribute broadcast=1 needs to be passed to enable broadcasting.
    - 
    - ### Attributes
    - 
    - * **axis - INT** :
    - 
    -   If set, defines the broadcast dimensions. See doc for details.
    - 
    - * **broadcast - INT** (default is '0'):
    - 
    -   Pass 1 to enable broadcasting
  
      ### Inputs
  
      - **X** (heterogeneous) - **T**:
  
    -   Input tensor of any shape, base of the exponent.
    +   First operand, base of the exponent.
    - - **Y** (heterogeneous) - **T**:
    + - **Y** (heterogeneous) - **T1**:
    ?                              +
  
    -   Input tensor of any shape broadcastable to X shape, the exponent component.
    +   Second operand, power of the exponent.
  
      ### Outputs
  
      - **Z** (heterogeneous) - **T**:
  
    -   Output tensor (same size as X)
    +   Output tensor
  
      ### Type Constraints
  
    - * **T** in ( tensor(double), tensor(float), tensor(float16) ):
    + * **T** in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64) ):
  
    -   Constrain input and output types to float tensors.+   Constrain input X and output types to float/int tensors.
    ?                  ++                          ++++         +
    + * **T1** in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
    + 
    +   Constrain input Y types to float/int tensors.
    `;

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