code: def load_model(model_uri, dst_path=None):
      """
      Obtains a model from MLFlow registry.
      :param model_uri: An unique identifier of the model within MLFlow registry.
      :param dst_path: (Optional) A temporary folder for downloading the persisted form of the model.
      :return: A model making predictions on Pandas dataframe.
      """
      path = _download_artifact_from_uri(
          artifact_uri=model_uri, output_path=dst_path
      )
      return _load_model(path)
  
imports: import logging
  import os
  import tempfile
  import pandas
  import subprocess
  import sys
  import yaml
  import mlflow
  from mlflow import pyfunc
  from mlflow.models import Model
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.utils import _save_example
  from mlflow.models import ModelSignature, ModelInputExample
  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 _get_flavor_configuration, _validate_and_copy_code_paths, _validate_and_prepare_target_save_path
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 9 for calling a global function with example usage: load_model(model_uri, dst_path) and returns: _load_model