code: def convert_onnx_array_feature_extractor(
      operator, device, extra_config
  ):
      """
      Converter for `ai.onnx.ml.ArrayFeatureExtractor`.
  
      Args:
          operator: An operator wrapping a `ai.onnx.ml.ArrayFeatureExtractor` model
          device: String defining the type of device the converted operator should be run on
          extra_config: Extra configuration used to select the best conversion strategy
  
      Returns:
          A PyTorch model
      """
      assert operator is not None, "Cannot convert None operator"
  
      column_indices = []
      initializers = extra_config[constants.ONNX_INITIALIZERS]
      operator_inputs = operator.raw_operator.origin.input
      column_indices = None
      for input_ in operator_inputs:
          if input_ in initializers:
              assert (
                  column_indices is None
              ), "More than one ArrayFeatureExtractor input matches with stored initializers."
              column_indices = list(initializers[input_].int64_data)
              if len(column_indices) == 0:
                  # If we are here it means that the column indices were not int64.
                  column_indices = list(initializers[input_].int32_data)
              assert (
                  len(column_indices) > 0
              ), "Cannot convert ArrayFeatureExtractor with empty column indices."
  
      return ArrayFeatureExtractor(operator, column_indices, device)
  
label_desc: Use image node_id 1 for calling a global function with example usage: convert_onnx_array_feature_extractor(operator, device, extra_config) and returns: ArrayFeatureExtractor