code: def convert_sklearn_mlp_classifier(operator, device, extra_config):
      """
      Converter for `sklearn.neural_network.MLPClassifier`
  
      Args:
          operator: An operator wrapping a `sklearn.neural_network.MLPClassifier` 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"
  
      classes = operator.raw_operator.classes_
      if not all(
          [type(x) in [int, np.int32, np.int64] for x in classes]
      ):
          raise RuntimeError(
              "Hummingbird supports only integer labels for class labels."
          )
  
      activation = operator.raw_operator.activation
      weights = operator.raw_operator.coefs_
      biases = operator.raw_operator.intercepts_
  
      return MLPClassificationModel(
          operator, weights, biases, activation, classes, device
      )
  
imports: import numpy
  from onnxconverter_common.registration import register_converter
  from .._mlp_implementations import MLPModel, MLPClassificationModel
label_desc: Use image node_id 1 for calling a global function with example usage: convert_sklearn_mlp_classifier(operator, device, extra_config) and returns: MLPClassificationModel