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.
      """
      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,
      )
  
imports: import logging
  import os
  import pickle
  from typing import Any, Dict, Optional
  import flavor
  import numpy
  import pandas
  import sktime
  import yaml
  from sktime.utils.multiindex import flatten_multiindex
  import mlflow
  from mlflow import pyfunc
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import _save_example
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.tracking._model_registry import DEFAULT_AWAIT_MAX_SLEEP_SECONDS
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv, _validate_env_arguments
  from mlflow.utils.file_utils import write_to
  from mlflow.utils.model_utils import _add_code_from_conf_to_system_path, _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.utils.requirements_utils import _get_pinned_requirement
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