class_docstr: Batch of environments inside the TensorFlow graph.
  
  The batch of environments will be stepped and reset inside of the graph using
  a tf.py_func(). The current batch of observations, actions, rewards, and done
  flags are held in according variables.
code: def initialize(self, sess):
      model_loader = tf.train.Saver(
          var_list=tf.global_variables(
              scope="next_frame*"
          )  # pylint:disable=unexpected-keyword-arg
      )
      if tf.gfile.IsDirectory(self._model_dir):
          trainer_lib.restore_checkpoint(
              self._model_dir,
              saver=model_loader,
              sess=sess,
              must_restore=True,
          )
      else:
          model_loader.restore(sess=sess, save_path=self._model_dir)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import os
  import numpy
  from tensor2tensor.data_generators.gym_env import DummyWorldModelProblem
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.rl.envs import in_graph_batch_env
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling the SimulatedBatchEnv obj's underlying member method code with example usage: obj.initialize(sess) without return types