(l-onnx-doc-SequenceAt)=

# SequenceAt


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

## SequenceAt - 11

### Version

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

Outputs a tensor copy from the tensor at 'position' in 'input_sequence'.
Accepted range for 'position' is in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'.
Negative value means counting positions from the back.

### Inputs

- **input_sequence** (heterogeneous) - **S**:

  Input sequence.
- **position** (heterogeneous) - **I**:

  Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape).

### Outputs

- **tensor** (heterogeneous) - **T**:

  Output tensor at the specified position in the input sequence.

### Type Constraints

* **S** in ( `seq(tensor(bool))`, `seq(tensor(complex128))`, `seq(tensor(complex64))`, `seq(tensor(double))`, `seq(tensor(float))`, `seq(tensor(float16))`, `seq(tensor(int16))`, `seq(tensor(int32))`, `seq(tensor(int64))`, `seq(tensor(int8))`, `seq(tensor(string))`, `seq(tensor(uint16))`, `seq(tensor(uint32))`, `seq(tensor(uint64))`, `seq(tensor(uint8))` ):

  Constrain to any tensor type.
* **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 to any tensor type.
* **I** in ( `tensor(int32)`, `tensor(int64)` ):

  Constrain position to integral tensor. It must be a scalar(tensor of empty shape).