(l-onnx-doc-Size)=

# Size


(l-onnx-op-size-23)=

## Size - 23

### Version

- **name**: [Size (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Size)
- **domain**: `main`
- **since_version**: `23`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 23**.

### Summary

Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **size** (heterogeneous) - **T1**:

  Total number of elements of the input tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float4e2m1)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int4)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint4)`, `tensor(uint64)`, `tensor(uint8)` ):

  Input tensor can be of arbitrary type.
* **T1** in ( `tensor(int64)` ):

  Constrain output to int64 tensor, which should be a scalar though.

```{toctree}
text_diff_Size_21_23
```

(l-onnx-op-size-21)=

## Size - 21

### Version

- **name**: [Size (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Size)
- **domain**: `main`
- **since_version**: `21`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 21**.

### Summary

Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **size** (heterogeneous) - **T1**:

  Total number of elements of the input tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int4)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint4)`, `tensor(uint64)`, `tensor(uint8)` ):

  Input tensor can be of arbitrary type.
* **T1** in ( `tensor(int64)` ):

  Constrain output to int64 tensor, which should be a scalar though.

```{toctree}
text_diff_Size_19_23
text_diff_Size_19_21
```

(l-onnx-op-size-19)=

## Size - 19

### Version

- **name**: [Size (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Size)
- **domain**: `main`
- **since_version**: `19`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 19**.

### Summary

Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **size** (heterogeneous) - **T1**:

  Total number of elements of the input tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(float8e4m3fn)`, `tensor(float8e4m3fnuz)`, `tensor(float8e5m2)`, `tensor(float8e5m2fnuz)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Input tensor can be of arbitrary type.
* **T1** in ( `tensor(int64)` ):

  Constrain output to int64 tensor, which should be a scalar though.

```{toctree}
text_diff_Size_13_23
text_diff_Size_13_21
text_diff_Size_13_19
```

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

## Size - 13

### Version

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

Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **size** (heterogeneous) - **T1**:

  Total number of elements of the input tensor

### Type Constraints

* **T** in ( `tensor(bfloat16)`, `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Input tensor can be of arbitrary type.
* **T1** in ( `tensor(int64)` ):

  Constrain output to int64 tensor, which should be a scalar though.

```{toctree}
text_diff_Size_1_23
text_diff_Size_1_21
text_diff_Size_1_19
text_diff_Size_1_13
```

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

## Size - 1

### Version

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

Takes a tensor as input and outputs a int64 scalar that equals to the total number of elements of the input tensor.

### Inputs

- **data** (heterogeneous) - **T**:

  An input tensor.

### Outputs

- **size** (heterogeneous) - **T1**:

  Total number of elements of the input tensor

### Type Constraints

* **T** in ( `tensor(bool)`, `tensor(complex128)`, `tensor(complex64)`, `tensor(double)`, `tensor(float)`, `tensor(float16)`, `tensor(int16)`, `tensor(int32)`, `tensor(int64)`, `tensor(int8)`, `tensor(string)`, `tensor(uint16)`, `tensor(uint32)`, `tensor(uint64)`, `tensor(uint8)` ):

  Input tensor can be of arbitrary type.
* **T1** in ( `tensor(int64)` ):

  Constrain output to int64 tensor, which should be a scalar though.