(l-onnx-docai-onnx-ml-FeatureVectorizer)=

# ai.onnx.ml - FeatureVectorizer


(l-onnx-opai-onnx-ml-featurevectorizer-1)=

## FeatureVectorizer - 1 (ai.onnx.ml)

### Version

- **name**: [FeatureVectorizer (GitHub)](https://github.com/onnx/onnx/blob/main/docs/Operators-ml.md#ai.onnx.ml.FeatureVectorizer)
- **domain**: `ai.onnx.ml`
- **since_version**: `1`
- **function**: `False`
- **support_level**: `SupportType.COMMON`
- **shape inference**: `False`

This version of the operator has been available
**since version 1 of domain ai.onnx.ml**.

### Summary

Concatenates input tensors into one continuous output.<br>
All input shapes are 2-D and are concatenated along the second dimension. 1-D tensors are treated as [1,C].
Inputs are copied to the output maintaining the order of the input arguments.<br>
All inputs must be integers or floats, while the output will be all floating point values.

### Attributes

* **inputdimensions - INTS** :

  The size of each input in the input list

### Inputs

Between 1 and 2147483647 inputs.

- **X** (variadic, heterogeneous) - **T1**:

  An ordered collection of tensors, all with the same element type.

### Outputs

- **Y** (heterogeneous) - **tensor(float)**:

  The output array, elements ordered as the inputs.

### Type Constraints

* **T1** in ( `tensor(double)`, `tensor(float)`, `tensor(int32)`, `tensor(int64)` ):

  The input type must be a tensor of a numeric type.