(l-onnx-doc-Scatter)=

# Scatter


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

## Scatter - 11

### Version

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

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

### Summary

This operator is deprecated. Please use ScatterElements, which provides the same functionality.

Scatter takes three inputs `data`, `updates`, and `indices` of the same
rank r >= 1 and an optional attribute axis that identifies an axis of `data`
(by default, the outer-most axis, that is axis 0). The output of the operation
is produced by creating a copy of the input `data`, and then updating its value
to values specified by `updates` at specific index positions specified by
`indices`. Its output shape is the same as the shape of `data`.

For each entry in `updates`, the target index in `data` is obtained by combining
the corresponding entry in `indices` with the index of the entry itself: the
index-value for dimension = axis is obtained from the value of the corresponding
entry in `indices` and the index-value for dimension != axis is obtained from the
index of the entry itself.

For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
is performed as below:
```
  output[indices[i][j]][j] = updates[i][j] if axis = 0,
  output[i][indices[i][j]] = updates[i][j] if axis = 1,
```

This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.

Example 1:
```
  data = [
      [0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0],
  ]
  indices = [
      [1, 0, 2],
      [0, 2, 1],
  ]
  updates = [
      [1.0, 1.1, 1.2],
      [2.0, 2.1, 2.2],
  ]
  output = [
      [2.0, 1.1, 0.0]
      [1.0, 0.0, 2.2]
      [0.0, 2.1, 1.2]
  ]
```
Example 2:
```
  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
  indices = [[1, 3]]
  updates = [[1.1, 2.1]]
  axis = 1
  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
```

### Attributes

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

  Which axis to scatter 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 r >= 1 (same rank as input). 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.
- **updates** (heterogeneous) - **T**:

  Tensor of rank r >=1 (same rank and shape as indices)

### Outputs

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

  Tensor of rank r >= 1 (same rank 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)` ):

  Input and output types can be of any tensor type.
* **Tind** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain indices to integer types

```{toctree}
text_diff_Scatter_9_11
```

(l-onnx-op-scatter-9)=

## Scatter - 9

### Version

- **name**: [Scatter (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators.md#Scatter)
- **domain**: `main`
- **since_version**: `9`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `True`

This version of the operator has been available
**since version 9**.

### Summary

Given `data`, `updates` and `indices` input tensors of rank r >= 1, write the values provided by `updates`
into the first input, `data`, along `axis` dimension of `data` (by default outer-most one as axis=0) at corresponding `indices`.
For each entry in `updates`, the target index in `data` is specified by corresponding entry in `indices`
for dimension = axis, and index in source for dimension != axis. For instance, in a 2-D tensor case,
data[indices[i][j]][j] = updates[i][j] if axis = 0, or data[i][indices[i][j]] = updates[i][j] if axis = 1,
where i and j are loop counters from 0 up to the respective size in `updates` - 1.
Example 1:
  data = [
      [0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0],
  ]
  indices = [
      [1, 0, 2],
      [0, 2, 1],
  ]
  updates = [
      [1.0, 1.1, 1.2],
      [2.0, 2.1, 2.2],
  ]
  output = [
      [2.0, 1.1, 0.0]
      [1.0, 0.0, 2.2]
      [0.0, 2.1, 1.2]
  ]
Example 2:
  data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
  indices = [[1, 3]]
  updates = [[1.1, 2.1]]
  axis = 1
  output = [[1.0, 1.1, 3.0, 2.1, 5.0]]

### Attributes

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

  Which axis to scatter 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 r >= 1 (same rank as input).
- **updates** (heterogeneous) - **T**:

  Tensor of rank r >=1 (same rank and shape as indices)

### Outputs

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

  Tensor of rank r >= 1 (same rank 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)` ):

  Input and output types can be of any tensor type.
* **Tind** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain indices to integer types