(l-onnx-doc-Sqrt)=

# Sqrt


(l-onnx-op-sqrt-13)=

## Sqrt - 13

### Version

- **name**: [Sqrt (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sqrt)
- **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

Square root takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the square root is, y = x^0.5, is applied to
the tensor elementwise. If x is negative, then it will return NaN.

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.

```{toctree}
text_diff_Sqrt_6_13
```

(l-onnx-op-sqrt-6)=

## Sqrt - 6

### Version

- **name**: [Sqrt (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sqrt)
- **domain**: `main`
- **since_version**: `6`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 6**.

### Summary

Square root takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the square root is, y = x^0.5, is applied to
the tensor elementwise. If x is negative, then it will return NaN.

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.

```{toctree}
text_diff_Sqrt_1_13
text_diff_Sqrt_1_6
```

(l-onnx-op-sqrt-1)=

## Sqrt - 1

### Version

- **name**: [Sqrt (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Sqrt)
- **domain**: `main`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

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

### Summary

Square root takes one input data (Tensor<T>) and produces one output data
(Tensor<T>) where the square root is, y = x^0.5, is applied to
the tensor elementwise. If x is negative, then it will return NaN.

### Attributes

* **consumed_inputs - INTS** :

  legacy optimization attribute.

### Inputs

- **X** (heterogeneous) - **T**:

  Input tensor

### Outputs

- **Y** (heterogeneous) - **T**:

  Output tensor

### Type Constraints

* **T** in ( `tensor(double)`, `tensor(float)`, `tensor(float16)` ):

  Constrain input and output types to float tensors.