(l-onnx-doc-Reshape)=

# Reshape


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

## Reshape - 23

### Version

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

Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
dimension will be set explicitly to zero (i.e. not taken from input tensor).
Shape (second input) could be an empty shape, which means converting to a scalar.
The input tensor's shape and the output tensor's shape are required to have the same number of elements.

If the attribute 'allowzero' is set, it is invalid for the specified shape to
contain both a zero value and -1, as the value of the dimension corresponding
to -1 cannot be determined uniquely.

### Attributes

* **allowzero - INT** (default is `'0'`):

  (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.

### Inputs

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

  An input tensor.
- **shape** (heterogeneous) - **tensor(int64)**:

  Specified shape for output.

### Outputs

- **reshaped** (heterogeneous) - **T**:

  Reshaped data.

### 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.

```{toctree}
text_diff_Reshape_21_23
```

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

## Reshape - 21

### Version

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

Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
dimension will be set explicitly to zero (i.e. not taken from input tensor).
Shape (second input) could be an empty shape, which means converting to a scalar.
The input tensor's shape and the output tensor's shape are required to have the same number of elements.

If the attribute 'allowzero' is set, it is invalid for the specified shape to
contain both a zero value and -1, as the value of the dimension corresponding
to -1 cannot be determined uniquely.

### Attributes

* **allowzero - INT** (default is `'0'`):

  (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.

### Inputs

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

  An input tensor.
- **shape** (heterogeneous) - **tensor(int64)**:

  Specified shape for output.

### Outputs

- **reshaped** (heterogeneous) - **T**:

  Reshaped data.

### 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.

```{toctree}
text_diff_Reshape_19_23
text_diff_Reshape_19_21
```

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

## Reshape - 19

### Version

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

Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
dimension will be set explicitly to zero (i.e. not taken from input tensor).
Shape (second input) could be an empty shape, which means converting to a scalar.
The input tensor's shape and the output tensor's shape are required to have the same number of elements.

If the attribute 'allowzero' is set, it is invalid for the specified shape to
contain both a zero value and -1, as the value of the dimension corresponding
to -1 cannot be determined uniquely.

### Attributes

* **allowzero - INT** (default is `'0'`):

  (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.

### Inputs

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

  An input tensor.
- **shape** (heterogeneous) - **tensor(int64)**:

  Specified shape for output.

### Outputs

- **reshaped** (heterogeneous) - **T**:

  Reshaped data.

### 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)` ):

  Constrain input and output types to all tensor types.

```{toctree}
text_diff_Reshape_14_23
text_diff_Reshape_14_21
text_diff_Reshape_14_19
```

(l-onnx-op-reshape-14)=

## Reshape - 14

### Version

- **name**: [Reshape (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Reshape)
- **domain**: `main`
- **since_version**: `14`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 14**.

### Summary

Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). If 'allowzero' is set, and the new shape includes 0, the
dimension will be set explicitly to zero (i.e. not taken from input tensor).
Shape (second input) could be an empty shape, which means converting to a scalar.
The input tensor's shape and the output tensor's shape are required to have the same number of elements.

If the attribute 'allowzero' is set, it is invalid for the specified shape to
contain both a zero value and -1, as the value of the dimension corresponding
to -1 cannot be determined uniquely.

### Attributes

* **allowzero - INT** (default is `'0'`):

  (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy.

### Inputs

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

  An input tensor.
- **shape** (heterogeneous) - **tensor(int64)**:

  Specified shape for output.

### Outputs

- **reshaped** (heterogeneous) - **T**:

  Reshaped data.

### 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_Reshape_13_23
text_diff_Reshape_13_21
text_diff_Reshape_13_19
text_diff_Reshape_13_14
```

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

## Reshape - 13

### Version

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

Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
The input tensor's shape and the output tensor's shape are required to have the same number of elements.

### Inputs

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

  An input tensor.
- **shape** (heterogeneous) - **tensor(int64)**:

  Specified shape for output.

### Outputs

- **reshaped** (heterogeneous) - **T**:

  Reshaped data.

### 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_Reshape_5_23
text_diff_Reshape_5_21
text_diff_Reshape_5_19
text_diff_Reshape_5_14
text_diff_Reshape_5_13
```

(l-onnx-op-reshape-5)=

## Reshape - 5

### Version

- **name**: [Reshape (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Reshape)
- **domain**: `main`
- **since_version**: `5`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 5**.

### Summary

Reshape the input tensor similar to numpy.reshape.
First input is the data tensor, second input is a shape tensor which specifies the output shape. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
The input tensor's shape and the output tensor's shape are required to have the same number of elements.

### Inputs

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

  An input tensor.
- **shape** (heterogeneous) - **tensor(int64)**:

  Specified shape for output.

### Outputs

- **reshaped** (heterogeneous) - **T**:

  Reshaped data.

### 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_Reshape_1_23
text_diff_Reshape_1_21
text_diff_Reshape_1_19
text_diff_Reshape_1_14
text_diff_Reshape_1_13
text_diff_Reshape_1_5
```

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

## Reshape - 1

### Version

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

Reshape the input tensor similar to numpy.reshape.
It takes a tensor as input and an argument `shape`. It outputs the reshaped tensor.
At most one dimension of the new shape can be -1. In this case, the value is
inferred from the size of the tensor and the remaining dimensions. A dimension
could also be 0, in which case the actual dimension value is unchanged (i.e. taken
from the input tensor). Shape (second input) could be an empty shape, which means converting to a scalar.
The input tensor's shape and the output tensor's shape are required to have the same number of elements.

### Attributes

* **consumed_inputs - INTS** :

  legacy optimization attribute.

* **shape - INTS** :

  New shape

### Inputs

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

  An input tensor.

### Outputs

- **reshaped** (heterogeneous) - **T**:

  Reshaped data.

### Type Constraints

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

  Constrain input and output types to float tensors.