class_docstr: Helper class to create a model with added dependency wheels from an existing registered model.
  The `wheeled` model contains all the model dependencies as wheels stored as model artifacts.
  .. note::
      This utility only operates on a model that has been registered to the Model Registry.
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docstr: Modifies the MLModel file to reflect updated information such as the run_id,
  utc_time_created. Additionally, this also adds `wheels` to the MLModel file to indicate that
  this is a `wheeled` model.
  :param original_model_file_path: The model metadata stored in the original MLmodel file.
  :param mlflow_model: :py:mod:`mlflow.models.Model` configuration of the newly created
                          wheeled model
docstr_tok: ["Modifies","the","MLModel","file","to","reflect","updated","information","such","as","the","run_id",",","utc_time_created",".","Additionally",",","this","also","adds","`","wheels","`","to","the","MLModel","file","to","indicate","that","this","is","a","`","wheeled","`","model",".",":","param","original_model_file_path",":","The","model","metadata","stored","in","the","original","MLmodel","file",".",":","param","mlflow_model",":",":","py",":","mod",":","`","mlflow.models.Model","`","configuration","of","the","newly","created","wheeled","model"]
code: def _update_mlflow_model(self, original_model_metadata, mlflow_model):
      """
      Modifies the MLModel file to reflect updated information such as the run_id,
      utc_time_created. Additionally, this also adds `wheels` to the MLModel file to indicate that
      this is a `wheeled` model.
      :param original_model_file_path: The model metadata stored in the original MLmodel file.
      :param mlflow_model: :py:mod:`mlflow.models.Model` configuration of the newly created
                              wheeled model
      """
  
      run_id = mlflow.tracking.fluent._get_or_start_run().info.run_id
      if mlflow_model is None:
          mlflow_model = Model(run_id=run_id)
  
      original_model_metadata.__dict__.update(
          {k: v for k, v in mlflow_model.__dict__.items() if v}
      )
      mlflow_model.__dict__.update(original_model_metadata.__dict__)
      mlflow_model.artifact_path = WheeledModel.get_wheel_artifact_path(
          mlflow_model.artifact_path
      )
  
      mlflow_model.wheels = {_PLATFORM: platform.platform()}
      return mlflow_model
  
imports: import os
  import platform
  import shutil
  import subprocess
  import sys
  import yaml
  import mlflow
  from mlflow import MlflowClient
  from mlflow.environment_variables import MLFLOW_WHEELED_MODEL_PIP_DOWNLOAD_OPTIONS
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import BAD_REQUEST
  from mlflow.pyfunc.model import MLMODEL_FILE_NAME, Model
  from mlflow.store.artifact.utils.models import _parse_model_uri, get_model_name_and_version
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.annotations import experimental
  from mlflow.utils.environment import _REQUIREMENTS_FILE_NAME, _get_pip_deps, _mlflow_additional_pip_env, _overwrite_pip_deps
  from mlflow.utils.model_utils import _validate_and_prepare_target_save_path
  from mlflow.utils.uri import get_databricks_profile_uri_from_artifact_uri
label_desc: Use image node_id 5 for calling the WheeledModel obj's underlying member method code with example usage: obj._update_mlflow_model(original_model_metadata, mlflow_model) and returns: mlflow_model