code: def collect_frames_for_random_starts(
      storage_env,
      stacked_env,
      agent,
      frame_stack_size,
      random_starts_step_limit,
      log_every_steps=None,
  ):
      """Collects frames from real env for random starts of simulated env."""
      del frame_stack_size
      storage_env.start_new_epoch(0)
      tf.logging.info(
          "Collecting %d frames for random starts.",
          random_starts_step_limit,
      )
      rl_utils.run_rollouts(
          stacked_env,
          agent,
          stacked_env.reset(),
          step_limit=random_starts_step_limit,
          many_rollouts_from_each_env=True,
          log_every_steps=log_every_steps,
      )
      # Save unfinished rollouts to history.
      stacked_env.reset()
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import datetime
  import os
  from tensor2tensor.data_generators import gym_env
  from tensor2tensor.layers import common_video
  from tensor2tensor.models.research import rl
  from tensor2tensor.rl import rl_utils
  from tensor2tensor.rl import trainer_model_based_params
  from tensor2tensor.utils import flags
  from tensor2tensor.utils import hparam
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
label_desc: Use image node_id 16 for calling a global function with example usage: collect_frames_for_random_starts(storage_env, stacked_env, agent, frame_stack_size, random_starts_step_limit, log_every_steps) without return types