(l-onnx-doc-ConcatFromSequence)=

# ConcatFromSequence


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

## ConcatFromSequence - 11

### Version

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

Concatenate a sequence of tensors into a single tensor.
All input tensors must have the same shape, except for the dimension size of the axis to concatenate on.
By default 'new_axis' is 0, the behavior is similar to numpy.concatenate.
When 'new_axis' is 1, the behavior is similar to numpy.stack.

### Attributes

* **axis - INT** (required) :

  Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`.

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

  Insert and concatenate on a new axis or not, default 0 means do not insert new axis.

### Inputs

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

  Sequence of tensors for concatenation

### Outputs

- **concat_result** (heterogeneous) - **T**:

  Concatenated tensor

### 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 input types 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 output types to any tensor type.