code: def convert(
      model, backend, test_input=None, device="cpu", extra_config={}
  ):
      """
      This function converts the specified input *model* into an implementation targeting *backend*.
      *Convert* supports [Sklearn], [LightGBM], [XGBoost], [ONNX], and [SparkML] models.
      For *LightGBM* and *XGBoost* currently only the Sklearn API is supported.
      The detailed list of models and backends can be found at `hummingbird.ml.supported`.
      The *onnx* backend works best with a test_input, but we try to generate one if none is provided.
      The *torch.jit* and *tvm* backends require a test_input.
      For *tvm* backend, the output container can do prediction only on the test data with the same size as test_input.
      [Sklearn]: https://scikit-learn.org/
      [LightGBM]: https://lightgbm.readthedocs.io/
      [XGBoost]: https://xgboost.readthedocs.io/
      [ONNX]: https://onnx.ai/
      [ONNX-ML]: https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md
      [ONNX operators]: https://github.com/onnx/onnx/blob/master/docs/Operators.md
      [Spark-ML]: https://spark.apache.org/docs/latest/api/python/pyspark.ml.html
  
      Args:
          model: An input model
          backend: The target for the conversion
          test_input: Some input data used to trace the model execution.
                      Multiple inputs can be passed as `tuple` objects or pandas Dataframes.
                      When possible, (`numpy`)`arrays` are suggested.
                      The number of rows becomes the batch size when tracing PyTorch models and compiling with TVM.
          device: The target device the model should be run. This parameter is only used by the *torch** backends and *tvm*, and
                  the devices supported are the one supported by PyTorch, i.e., 'cpu' or 'cuda'.
          extra_config: Extra configurations to be used by the individual operator converters.
                        The set of supported extra configurations can be found at `hummingbird.ml.supported`
  
      Examples:
          >>> pytorch_model = convert(sklearn_model,`torch`)
  
      Returns:
          A model implemented in *backend*, which is equivalent to the input model
      """
      assert constants.REMAINDER_SIZE not in extra_config
      return _convert_common(
          model, backend, test_input, device, extra_config
      )
  
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 11 for calling a global function with example usage: convert(model, backend, test_input, device, extra_config) and returns: _convert_common