code: def _load_model(path, serialization_format):
      _check_mlflow_dependencies(severity="error")
      from mlflow.exceptions import MlflowException
      from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  
      if serialization_format not in SUPPORTED_SERIALIZATION_FORMATS:
          raise MlflowException(
              message=(
                  "Unrecognized serialization format: {serialization_format}. "
                  "Please specify one of the following supported formats: "
                  "{supported_formats}.".format(
                      serialization_format=serialization_format,
                      supported_formats=SUPPORTED_SERIALIZATION_FORMATS,
                  )
              ),
              error_code=INVALID_PARAMETER_VALUE,
          )
  
      return load(path)
  
file_path: sktime/sktime/utils/mlflow_sktime.py
imports: import logging
  import os
  import pandas
  import yaml
  import sktime
  from sktime import utils
  from sktime.base._serialize import load
  from sktime.utils.multiindex import flatten_multiindex
  from sktime.utils.validation._dependencies import _check_mlflow_dependencies
label_desc: Use image node_id 7 for calling a global function with example usage: _load_model(path, serialization_format) and returns: load