# Pow¶

## Pow - 15¶

### Version¶

• name: Pow (GitHub)

• domain: main

• since_version: 15

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 15.

### Summary¶

Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) 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.

### Inputs¶

• X (heterogeneous) - T:

First operand, base of the exponent.

• Y (heterogeneous) - T1:

Second operand, power of the exponent.

### Outputs¶

• Z (heterogeneous) - T:

Output tensor

### Type Constraints¶

• T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64) ):

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.

## Pow - 13¶

### Version¶

• name: Pow (GitHub)

• domain: main

• since_version: 13

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 13.

### Summary¶

Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) 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.

### Inputs¶

• X (heterogeneous) - T:

First operand, base of the exponent.

• Y (heterogeneous) - T1:

Second operand, power of the exponent.

### Outputs¶

• Z (heterogeneous) - T:

Output tensor

### Type Constraints¶

• T in ( tensor(bfloat16), tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64) ):

Constrain input X and output types to float/int tensors.

• T1 in ( 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.

## Pow - 12¶

### Version¶

• name: Pow (GitHub)

• domain: main

• since_version: 12

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 12.

### Summary¶

Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) 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.

### Inputs¶

• X (heterogeneous) - T:

First operand, base of the exponent.

• Y (heterogeneous) - T1:

Second operand, power of the exponent.

### Outputs¶

• Z (heterogeneous) - T:

Output tensor.

### Type Constraints¶

• T in ( tensor(double), tensor(float), tensor(float16), tensor(int32), tensor(int64) ):

Constrain input X and output types to float/int tensors.

• T1 in ( 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.

## Pow - 7¶

### Version¶

• name: Pow (GitHub)

• domain: main

• since_version: 7

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 7.

### Summary¶

Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) 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.

### Inputs¶

• X (heterogeneous) - T:

First operand, base of the exponent.

• Y (heterogeneous) - T:

Second operand, power of the exponent.

### Outputs¶

• Z (heterogeneous) - T:

Output tensor.

### Type Constraints¶

• T in ( tensor(double), tensor(float), tensor(float16) ):

Constrain input and output types to float tensors.

## Pow - 1¶

### Version¶

• name: Pow (GitHub)

• domain: main

• since_version: 1

• function: False

• support_level: SupportType.COMMON

• shape inference: True

This version of the operator has been available since version 1.

### Summary¶

Pow takes input data (Tensor) and exponent Tensor, and produces one output data (Tensor) where the function f(x) = x^exponent, is applied to the data tensor elementwise.

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'):

### Inputs¶

• X (heterogeneous) - T:

Input tensor of any shape, base of the exponent.

• Y (heterogeneous) - T:

Input tensor of any shape broadcastable to X shape, the exponent component.

### Outputs¶

• Z (heterogeneous) - T:

Output tensor (same size as X)

### Type Constraints¶

• T in ( tensor(double), tensor(float), tensor(float16) ):

Constrain input and output types to float tensors.