code: def initialize_env_specs(hparams, env_problem_name):
      """Initializes env_specs using the appropriate env."""
      if env_problem_name:
          env = registry.env_problem(
              env_problem_name, batch_size=hparams.batch_size
          )
      else:
          env = rl_utils.setup_env(
              hparams,
              hparams.batch_size,
              hparams.eval_max_num_noops,
              hparams.rl_env_max_episode_steps,
              env_name=hparams.rl_env_name,
          )
          env.start_new_epoch(0)
  
      return rl.make_real_env_fn(env)
  
file_path: tensor2tensor/tensor2tensor/rl/trainer_model_free.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import pprint
  from tensor2tensor.models.research import rl
  from tensor2tensor.rl import rl_utils
  from tensor2tensor.utils import flags
  from tensor2tensor.utils import misc_utils
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_env_specs(hparams, env_problem_name) and returns: rl