(l-onnx-doc-Tile)=

# Tile


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

## Tile - 13

### Version

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

Constructs a tensor by tiling a given tensor.
This is the same as function `tile` in Numpy, but no broadcast.
For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]

### Inputs

- **input** (heterogeneous) - **T**:

  Input tensor of any shape.
- **repeats** (heterogeneous) - **T1**:

  1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.

### Outputs

- **output** (heterogeneous) - **T**:

  Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]

### 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.
* **T1** in ( `tensor(int64)` ):

  Constrain repeat's type to int64 tensors.

```{toctree}
text_diff_Tile_6_13
```

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

## Tile - 6

### Version

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

Constructs a tensor by tiling a given tensor.
This is the same as function `tile` in Numpy, but no broadcast.
For example A = [[1, 2], [3, 4]], B = [1, 2], tile(A, B) = [[1, 2, 1, 2], [3, 4, 3, 4]]

### Inputs

- **input** (heterogeneous) - **T**:

  Input tensor of any shape.
- **repeats** (heterogeneous) - **T1**:

  1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions.

### Outputs

- **output** (heterogeneous) - **T**:

  Output tensor of the same dimensions and type as tensor input. output_dim[i] = input_dim[i] * repeats[i]

### 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.
* **T1** in ( `tensor(int64)` ):

  Constrain repeat's type to int64 tensors.

```{toctree}
text_diff_Tile_1_13
text_diff_Tile_1_6
```

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

## Tile - 1

### Version

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

Repeat the elements of a tensor along an axis.

### Inputs

- **input** (heterogeneous) - **T**:

  Input tensor of any shape.
- **tiles** (heterogeneous) - **T**:

  Number of repeated copies to make of the input tensor.
- **axis** (heterogeneous) - **T**:

  Axis along which to repeat.

### Outputs

- **output** (heterogeneous) - **T**:

  Output tensor of same shape and type as input.

### Type Constraints

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

  Constrain input types to float tensors.
* **T1** in ( `tensor(int64)` ):

  Constrain tiles and axis's type to int64 tensors.