code: def store_imported_modules(cap_cm, model_path, flavor, output_file):
      # If `model_path` refers to an MLflow model directory, load the model using
      # `mlflow.pyfunc.load_model`
      if os.path.isdir(model_path) and MLMODEL_FILE_NAME in os.listdir(
          model_path
      ):
          mlflow_model = Model.load(model_path)
          pyfunc_conf = mlflow_model.flavors.get(
              mlflow.pyfunc.FLAVOR_NAME
          )
          input_example = mlflow_model.load_input_example(model_path)
          params = mlflow_model.load_input_example_params(model_path)
          loader_module = importlib.import_module(pyfunc_conf[MAIN])
          original = loader_module._load_pyfunc
  
          @functools.wraps(original)
          def _load_pyfunc_patch(*args, **kwargs):
              with cap_cm:
                  model = original(*args, **kwargs)
                  if input_example is not None:
                      model.predict(input_example, params=params)
                  return model
  
          loader_module._load_pyfunc = _load_pyfunc_patch
          try:
              mlflow.pyfunc.load_model(model_path)
          finally:
              loader_module._load_pyfunc = original
      # Otherwise, load the model using `mlflow.<flavor>._load_pyfunc`.
      # For models that don't contain pyfunc flavor (e.g. scikit-learn estimator
      # that doesn't implement a `predict` method),
      # we need to directly pass a model data path to this script.
      else:
          with cap_cm:
              importlib.import_module(f"mlflow.{flavor}")._load_pyfunc(
                  model_path
              )
  
      # Store the imported modules in `output_file`
      write_to(output_file, "\n".join(cap_cm.imported_modules))
  
imports: import argparse
  import builtins
  import functools
  import importlib
  import json
  import os
  import sys
  import mlflow
  from mlflow.models.model import MLMODEL_FILE_NAME, Model
  from mlflow.pyfunc import MAIN
  from mlflow.utils._spark_utils import _prepare_subprocess_environ_for_creating_local_spark_session
  from mlflow.utils.file_utils import write_to
  from mlflow.utils.requirements_utils import DATABRICKS_MODULES_TO_PACKAGES, MLFLOW_MODULES_TO_PACKAGES
label_desc: Use image node_id 4 for calling a global function with example usage: store_imported_modules(cap_cm, model_path, flavor, output_file) and returns: model