code: def _convert_sklearn(
      model, backend, test_input, device, extra_config={}
  ):
      """
      This function converts the specified *scikit-learn* (API) model into its *backend* counterpart.
      The supported operators and backends can be found at `hummingbird.ml.supported`.
      """
      assert model is not None
      if type(model).__name__ in [e[0] for e in all_estimators()]:
          assert check_is_fitted(estimator=model) is None
      assert_torch_installed()
  
      # Parse scikit-learn model as our internal data structure (i.e., Topology)
      # We modify the scikit learn model during translation.
      model = deepcopy(model)
      topology = parse_sklearn_api_model(model, extra_config)
  
      # Convert the Topology object into a PyTorch model.
      hb_model = topology_converter(
          topology,
          backend,
          test_input,
          device,
          extra_config=extra_config,
      )
      return hb_model
  
imports: from copy import deepcopy
  import psutil
  import numpy
  from .operator_converters import constants
  from ._parse import parse_sklearn_api_model, parse_onnx_api_model, parse_sparkml_api_model
  from ._topology import convert
  from ._utils import assert_torch_installed, assert_lightgbm_installed, assert_xgboost_installed, pandas_installed, sparkml_installed, is_pandas_dataframe, is_spark_dataframe, tvm_installed
  from .exceptions import MissingConverter, MissingBackend
  from .supported import backends
  from sklearn.utils import all_estimators
  from sklearn.utils.validation import check_is_fitted
  from .None import operator_converters
  from .supported import xgb_operator_list
  from .supported import lgbm_operator_list
label_desc: Use image node_id 5 for calling a global function with example usage: _convert_sklearn(model, backend, test_input, device, extra_config) and returns: hb_model