.. _op_ai_onnx_ml_FeatureVectorizer:

FeatureVectorizer
=================

- **Domain**: ``ai.onnx.ml``
- **Since version**: 1

Concatenates input tensors into one continuous output. 
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. 
All inputs must be integers or floats, while the output will be all floating point values.

**Inputs**

- **X** (*T1*): An ordered collection of tensors, all with the same element type.

**Outputs**

- **Y** (*tensor(float)*): The output array, elements ordered as the inputs.

**Type Constraints**

- **T1**: The input type must be a tensor of a numeric type.
  Allowed types: tensor(double), tensor(float), tensor(int32), tensor(int64).

Examples
--------

**test_cc_feature_vectorizer_mixed_dtypes**

.. code-block:: text

    Node:
      ai.onnx.ml.FeatureVectorizer(x0, x1) -> (y)
      Attributes:
        inputdimensions = [2, 2]

.. code-block:: text

    Inputs:
      x0: shape=(1, 2), dtype=int64
        [[1, 2]]
      x1: shape=(1, 2), dtype=float32
        [[3.5, 4.5]]

    Outputs:
      y: shape=(1, 4), dtype=float32
        [[1. , 2. , 3.5, 4.5]]

**test_cc_feature_vectorizer_two_float**

.. code-block:: text

    Node:
      ai.onnx.ml.FeatureVectorizer(x0, x1) -> (y)
      Attributes:
        inputdimensions = [2, 1]

.. code-block:: text

    Inputs:
      x0: shape=(2, 2), dtype=float32
        [[1., 2.],
         [3., 4.]]
      x1: shape=(2, 1), dtype=float32
        [[10.],
         [20.]]

    Outputs:
      y: shape=(2, 3), dtype=float32
        [[ 1.,  2., 10.],
         [ 3.,  4., 20.]]