code: def fetch_tf_model(dataset: str, model: str) -> tf.keras.Model:
      """
      Fetch pretrained tensorflow models from the google cloud bucket.
  
      Parameters
      ----------
      dataset
          Dataset trained on.
      model
          Model name.
  
      Returns
      -------
      Pretrained tensorflow model.
      """
      url = "https://storage.googleapis.com/seldon-models/alibi-detect/classifier/"
      path_model = _join_url(url, [dataset, model, "model.h5"])
      save_path = tf.keras.utils.get_file(
          Path(model + ".h5").resolve(), path_model
      )
      if dataset == "cifar10" and model == "resnet56":
          custom_objects = {"backend": backend}
      else:
          custom_objects = None
      clf = tf.keras.models.load_model(
          save_path, custom_objects=custom_objects
      )
      return clf
  
imports: import logging
  import os
  from io import BytesIO
  from pathlib import Path
  from typing import Union, TYPE_CHECKING, Tuple
  import dill
  import requests
  from requests import RequestException
  import tensorflow
  from tensorflow.python.keras import backend
  from alibi_detect.models.tensorflow import PixelCNN
  from alibi_detect.saving import load_detector
  from alibi_detect.utils.url import _join_url
label_desc: Use image node_id 2 for calling a global function with example usage: fetch_tf_model(dataset, model) and returns: clf