(l-onnx-doc-Unsqueeze)=

# Unsqueeze


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

## Unsqueeze - 23

### Version

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

Insert single-dimensional entries to the shape of an input tensor (`data`).
Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).

For example, given an input tensor (`data`) of shape [3, 4, 5], then
Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].

The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
The order of values in `axes` does not matter and can come in any order.

### Inputs

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

  Original tensor
- **axes** (heterogeneous) - **tensor(int64)**:

  List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).

### Outputs

- **expanded** (heterogeneous) - **T**:

  Reshaped tensor with same data as input.

### 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)` ):

  Constrain input and output types to all tensor types up to IRv11.

```{toctree}
text_diff_Unsqueeze_21_23
```

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

## Unsqueeze - 21

### Version

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

Insert single-dimensional entries to the shape of an input tensor (`data`).
Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).

For example, given an input tensor (`data`) of shape [3, 4, 5], then
Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].

The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
The order of values in `axes` does not matter and can come in any order.

### Inputs

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

  Original tensor
- **axes** (heterogeneous) - **tensor(int64)**:

  List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).

### Outputs

- **expanded** (heterogeneous) - **T**:

  Reshaped tensor with same data as input.

### 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)` ):

  Constrain input and output types to all tensor types up to IRv10.

```{toctree}
text_diff_Unsqueeze_13_23
text_diff_Unsqueeze_13_21
```

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

## Unsqueeze - 13

### Version

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

Insert single-dimensional entries to the shape of an input tensor (`data`).
Takes one required input `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).

For example, given an input tensor (`data`) of shape [3, 4, 5], then
Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].

The input `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
The order of values in `axes` does not matter and can come in any order.

### Inputs

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

  Original tensor
- **axes** (heterogeneous) - **tensor(int64)**:

  List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).

### Outputs

- **expanded** (heterogeneous) - **T**:

  Reshaped tensor with same data as input.

### 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)` ):

  Constrain input and output types to all tensor types.

```{toctree}
text_diff_Unsqueeze_11_23
text_diff_Unsqueeze_11_21
text_diff_Unsqueeze_11_13
```

(l-onnx-op-unsqueeze-11)=

## Unsqueeze - 11

### Version

- **name**: [Unsqueeze (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Unsqueeze)
- **domain**: `main`
- **since_version**: `11`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 11**.

### Summary

Insert single-dimensional entries to the shape of an input tensor (`data`).
Takes one required argument `axes` - which contains a list of dimension indices and this operator will insert a dimension of value `1` into the corresponding index of the output tensor (`expanded`).

For example:
  Given an input tensor (`data`) of shape [3, 4, 5], then
  Unsqueeze(data, axes=[0, 4]) outputs a tensor (`expanded`) containing same data as `data` but with shape [1, 3, 4, 5, 1].

The attribute `axes` should not contain any duplicate entries. It is an error if it contains duplicates.
The rank of the output tensor (`output_rank`) is the rank of the input tensor (`data`) plus the number of values in `axes`.
Each value in `axes` should be within the (inclusive) range [-output_rank , output_rank - 1].
The order of values in `axes` does not matter and can come in any order.

### Attributes

* **axes - INTS** (required) :

  List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded).

### Inputs

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

  Original tensor

### Outputs

- **expanded** (heterogeneous) - **T**:

  Reshaped tensor with same data as input.

### 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)` ):

  Constrain input and output types to all tensor types.

```{toctree}
text_diff_Unsqueeze_1_23
text_diff_Unsqueeze_1_21
text_diff_Unsqueeze_1_13
text_diff_Unsqueeze_1_11
```

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

## Unsqueeze - 1

### Version

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

Insert single-dimensional entries to the shape of a tensor.
Takes one required argument `axes`, a list of dimensions that will be inserted.
Dimension indices in `axes` are as seen in the output tensor. For example:
  Given a tensor such that tensor with shape [3, 4, 5], then
  Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]

### Attributes

* **axes - INTS** (required) :

  List of non-negative integers, indicate the dimensions to be inserted

### Inputs

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

  Original tensor

### Outputs

- **expanded** (heterogeneous) - **T**:

  Reshaped tensor with same data as input.

### 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)` ):

  Constrain input and output types to all tensor types.