code: def load_model(model_uri, dst_path=None):
      """Load a sktime model from a local file or a run.
  
      Parameters
      ----------
      model_uri : str
          The location, in URI format, of the MLflow model. For example:
  
                      - ``/Users/me/path/to/local/model``
                      - ``relative/path/to/local/model``
                      - ``s3://my_bucket/path/to/model``
                      - ``runs:/<mlflow_run_id>/run-relative/path/to/model``
                      - ``mlflow-artifacts:/path/to/model``
  
          For more information about supported URI schemes, see
          `Referencing Artifacts <https://www.mlflow.org/docs/latest/tracking.html#
          artifact-locations>`_.
      dst_path : str, optional (default=None)
          The local filesystem path to which to download the model artifact.This
          directory must already exist. If unspecified, a local output path will
          be created.
  
      Returns
      -------
      A sktime model instance.
  
      References
      ----------
      .. [1] https://www.mlflow.org/docs/latest/python_api/mlflow.models.html#mlflow.models.Model.load
  
      Examples
      --------
      >>> from sktime.datasets import load_airline
      >>> from sktime.forecasting.arima import ARIMA
      >>> from sktime.utils import mlflow_sktime  # doctest: +SKIP
      >>> y = load_airline()  # doctest: +SKIP
      >>> forecaster = ARIMA(  # doctest: +SKIP
      ...     order=(1, 1, 0),
      ...     seasonal_order=(0, 1, 0, 12),
      ...     suppress_warnings=True)
      >>> forecaster.fit(y)  # doctest: +SKIP
      ARIMA(...)
      >>> model_path = "model"  # doctest: +SKIP
      >>> mlflow_sktime.save_model(  # doctest: +SKIP
      ...     sktime_model=forecaster,
      ...     path=model_path)
      >>> loaded_model = mlflow_sktime.load_model(model_uri=model_path)  # doctest: +SKIP
      """  # noqa: E501
      _check_mlflow_dependencies(severity="error")
      from mlflow.tracking.artifact_utils import (
          _download_artifact_from_uri,
      )
      from mlflow.utils.model_utils import (
          _add_code_from_conf_to_system_path,
          _get_flavor_configuration,
      )
  
      local_model_path = _download_artifact_from_uri(
          artifact_uri=model_uri, output_path=dst_path
      )
      flavor_conf = _get_flavor_configuration(
          model_path=local_model_path, flavor_name=FLAVOR_NAME
      )
      _add_code_from_conf_to_system_path(local_model_path, flavor_conf)
      sktime_model_file_path = os.path.join(
          local_model_path, flavor_conf["pickled_model"]
      )
      serialization_format = flavor_conf.get(
          "serialization_format", SERIALIZATION_FORMAT_PICKLE
      )
      return _load_model(
          path=sktime_model_file_path,
          serialization_format=serialization_format,
      )
  
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 5 for calling a global function with example usage: load_model(model_uri, dst_path) and returns: _load_model