code: def download_model(alg, env, model_type="best"):
      """Downloads and returns pretrained model.
  
      Args:
          alg (string): URL to download from.
          env (string): Gym Environment name.
          model_type (string): Either `best` or `final`.
      Returns:
          str: Path to the downloaded file.
          bool: whether the model was already cached.
      """
      assert alg in MODELS, "No pretrained models for " + alg + "."
      assert model_type in MODELS[alg], (
          'Model type "' + model_type + '" is not supported.'
      )
      env = env.replace("NoFrameskip-v4", "")
      model_path, is_cached = download_and_store_model(
          alg, download_url, env, model_type
      )
      return model_path, is_cached
  
file_path: pfrl/pfrl/utils/pretrained_models.py
imports: import hashlib
  import os
  import posixpath
  import shutil
  import sys
  import tempfile
  import time
  import urllib
  import zipfile
  import filelock
label_desc: Use image node_id 5 for calling a global function with example usage: download_model(alg, env, model_type) and returns: model_path,  is_cached