code: def convert_sklearn_array_feature_extractor(
      operator, device, extra_config
  ):
      """
      Converter for ArrayFeatureExtractor.
  
      Args:
          operator: An operator wrapping a ArrayFeatureExtractor operator
          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"
  
      indices = operator.column_indices
      if any([type(i) is bool for i in indices]):
          indices = [i for i in range(len(indices)) if indices[i]]
      return ArrayFeatureExtractor(
          operator, np.ascontiguousarray(indices), device
      )
  
file_path: hummingbird/hummingbird/ml/operator_converters/sklearn/pipeline.py
imports: import numpy
  from onnxconverter_common.registration import register_converter
  import torch
  from ..None import constants
  from .._array_feature_extractor_implementations import ArrayFeatureExtractor
  from .._physical_operator import PhysicalOperator
  from .._pipeline_implementations import Concat
label_desc: Use image node_id 1 for calling a global function with example usage: convert_sklearn_array_feature_extractor(operator, device, extra_config) and returns: ArrayFeatureExtractor