(l-onnx-doc-Gather)=

# Gather


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

## Gather - 13

### Version

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

Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
them in an output tensor of rank q + (r - 1).

If `axis = 0`, let `k = indices[i_{0}, ..., i_{q-1}]`
then `output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]`:

```
data = [
    [1.0, 1.2],
    [2.3, 3.4],
    [4.5, 5.7],
]
indices = [
    [0, 1],
    [1, 2],
]
output = [
    [
        [1.0, 1.2],
        [2.3, 3.4],
    ],
    [
        [2.3, 3.4],
        [4.5, 5.7],
    ],
]
```

If `axis = 1`, let `k = indices[i_{0}, ..., i_{q-1}]`
then `output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]`:

```
data = [
    [1.0, 1.2, 1.9],
    [2.3, 3.4, 3.9],
    [4.5, 5.7, 5.9],
]
indices = [
    [0, 2],
]
axis = 1,
output = [
        [[1.0, 1.9]],
        [[2.3, 3.9]],
        [[4.5, 5.9]],
]
```

### Attributes

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

  Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).

### Inputs

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

  Tensor of rank r >= 1.
- **indices** (heterogeneous) - **Tind**:

  Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.

### Outputs

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

  Tensor of rank q + (r - 1).

### 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 any tensor type.
* **Tind** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain indices to integer types

```{toctree}
text_diff_Gather_11_13
```

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

## Gather - 11

### Version

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

Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
them in an output tensor of rank q + (r - 1).

axis = 0 :

Let
k = indices[i_{0}, ..., i_{q-1}]
Then
output[i_{0}, ..., i_{q-1}, j_{0}, ..., j_{r-2}] = input[k , j_{0}, ..., j_{r-2}]

```
  data = [
      [1.0, 1.2],
      [2.3, 3.4],
      [4.5, 5.7],
  ]
  indices = [
      [0, 1],
      [1, 2],
  ]
  output = [
      [
          [1.0, 1.2],
          [2.3, 3.4],
      ],
      [
          [2.3, 3.4],
          [4.5, 5.7],
      ],
  ]
```
axis = 1 :

Let
k = indices[i_{0}, ..., i_{q-1}]
Then
output[j_{0}, i_{0}, ..., i_{q-1}, j_{1}, ..., j_{r-2}] = input[j_{0}, k, j_{1}, ..., j_{r-2}]

```
  data = [
      [1.0, 1.2, 1.9],
      [2.3, 3.4, 3.9],
      [4.5, 5.7, 5.9],
  ]
  indices = [
      [0, 2],
  ]
  axis = 1,
  output = [
      [[1.0, 1.9]],
      [[2.3, 3.9]],
      [[4.5, 5.9]],
  ]
```

### Attributes

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

  Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).

### Inputs

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

  Tensor of rank r >= 1.
- **indices** (heterogeneous) - **Tind**:

  Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.

### Outputs

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

  Tensor of rank q + (r - 1).

### 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 any tensor type.
* **Tind** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain indices to integer types

```{toctree}
text_diff_Gather_1_13
text_diff_Gather_1_11
```

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

## Gather - 1

### Version

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

Given `data` tensor of rank r >= 1, and `indices` tensor of rank q, gather
entries of the axis dimension of `data` (by default outer-most one as axis=0) indexed by `indices`, and concatenates
them in an output tensor of rank q + (r - 1).
Example 1:
```
  data = [
      [1.0, 1.2],
      [2.3, 3.4],
      [4.5, 5.7],
  ]
  indices = [
      [0, 1],
      [1, 2],
  ]
  output = [
      [
          [1.0, 1.2],
          [2.3, 3.4],
      ],
      [
          [2.3, 3.4],
          [4.5, 5.7],
      ],
  ]
```
Example 2:
```
  data = [
      [1.0, 1.2, 1.9],
      [2.3, 3.4, 3.9],
      [4.5, 5.7, 5.9],
  ]
  indices = [
      [0, 2],
  ]
  axis = 1,
  output = [
      [[1.0, 1.9]],
      [[2.3, 3.9]],
      [[4.5, 5.9]],
  ]
```

### Attributes

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

  Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1]

### Inputs

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

  Tensor of rank r >= 1.
- **indices** (heterogeneous) - **Tind**:

  Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds.

### Outputs

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

  Tensor of rank q + (r - 1).

### 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 any tensor type.
* **Tind** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain indices to integer types