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

# ai.onnx.ml - ArrayFeatureExtractor


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

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

### Version

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

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

### Summary

Select elements of the input tensor based on the indices passed.<br>
The indices are applied to the last axes of the tensor.

### Inputs

- **X** (heterogeneous) - **T**:

  Data to be selected
- **Y** (heterogeneous) - **tensor(int64)**:

  The indices, based on 0 as the first index of any dimension.

### Outputs

- **Z** (heterogeneous) - **T**:

  Selected output data as an array

### Type Constraints

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

  The input must be a tensor of a numeric type or string. The output will be of the same tensor type.

### Examples

#### default

```python
import numpy as np
import onnx

node = onnx.helper.make_node(
    "ArrayFeatureExtractor",
    inputs=["x", "y"],
    outputs=["z"],
    domain="ai.onnx.ml",
)

x = np.arange(12).reshape((3, 4)).astype(np.float32)
y = np.array([0, 1], dtype=np.int64)
z = np.array([[0, 4, 8], [1, 5, 9]], dtype=np.float32).T
expect(
    node,
    inputs=[x, y],
    outputs=[z],
    name="test_ai_onnx_ml_array_feature_extractor",
)
```