code: def prepare_env(
      model_uri,
      env_manager,
      install_mlflow,
  ):
      """
      Performs any preparation necessary to predict or serve the model, for example
      downloading dependencies or initializing a conda environment. After preparation,
      calling predict or serve should be fast.
      """
      return get_flavor_backend(
          model_uri,
          env_manager=env_manager,
          install_mlflow=install_mlflow,
      ).prepare_env(model_uri=model_uri)
  
file_path: mlflow/mlflow/models/cli.py
imports: import logging
  import click
  from mlflow.models import python_api
  from mlflow.models.flavor_backend_registry import get_flavor_backend
  from mlflow.utils import cli_args
  from mlflow.utils import env_manager
label_desc: Use image node_id 4 for calling a global function with example usage: prepare_env(model_uri, env_manager, install_mlflow) and returns: get_flavor_backend