code: def collect(
      summary_dir: Text,
      environment_name: Text,
      collect_policy: py_tf_eager_policy.PyTFEagerPolicyBase,
      replay_buffer_server_address: Text,
      variable_container_server_address: Text,
      suite_load_fn: Callable[
          [Text], py_environment.PyEnvironment
      ] = suite_mujoco.load,
      initial_collect_steps: int = 10000,
      max_train_steps: int = 2000000,
  ) -> None:
      """Collects experience using a policy updated after every episode."""
      # Create the environment. For now support only single environment collection.
      collect_env = suite_load_fn(environment_name)
  
      # Create the variable container.
      train_step = train_utils.create_train_step()
      variables = {
          reverb_variable_container.POLICY_KEY: collect_policy.variables(),
          reverb_variable_container.TRAIN_STEP_KEY: train_step,
      }
      variable_container = (
          reverb_variable_container.ReverbVariableContainer(
              variable_container_server_address,
              table_names=[reverb_variable_container.DEFAULT_TABLE],
          )
      )
      variable_container.update(variables)
  
      # Create the replay buffer observer.
      rb_observer = reverb_utils.ReverbAddTrajectoryObserver(
          reverb.Client(replay_buffer_server_address),
          table_name=reverb_replay_buffer.DEFAULT_TABLE,
          sequence_length=2,
          stride_length=1,
      )
  
      random_policy = random_py_policy.RandomPyPolicy(
          collect_env.time_step_spec(), collect_env.action_spec()
      )
      initial_collect_actor = actor.Actor(
          collect_env,
          random_policy,
          train_step,
          steps_per_run=initial_collect_steps,
          observers=[rb_observer],
      )
      logging.info("Doing initial collect.")
      initial_collect_actor.run()
  
      env_step_metric = py_metrics.EnvironmentSteps()
      collect_actor = actor.Actor(
          collect_env,
          collect_policy,
          train_step,
          steps_per_run=1,
          metrics=actor.collect_metrics(10),
          summary_dir=summary_dir,
          observers=[rb_observer, env_step_metric],
      )
  
      # Run the experience collection loop.
      while train_step.numpy() < max_train_steps:
          logging.info(
              "Collecting with policy at step: %d", train_step.numpy()
          )
          collect_actor.run()
          variable_container.update(variables)
  
imports: import os
  from typing import Callable, Text
  from absl import app
  from absl import flags
  from absl import logging
  import gin
  import reverb
  import tensorflow.compat.v2
  from tf_agents.environments import py_environment
  from tf_agents.environments import suite_mujoco
  from tf_agents.experimental.distributed import reverb_variable_container
  from tf_agents.metrics import py_metrics
  from tf_agents.policies import py_tf_eager_policy
  from tf_agents.policies import random_py_policy
  from tf_agents.replay_buffers import reverb_replay_buffer
  from tf_agents.replay_buffers import reverb_utils
  from tf_agents.system import system_multiprocessing
  from tf_agents.train import actor
  from tf_agents.train import learner
  from tf_agents.train.utils import train_utils
label_desc: Use image node_id 1 for calling a global function with example usage: collect(summary_dir, environment_name, collect_policy, replay_buffer_server_address, variable_container_server_address, suite_load_fn, initial_collect_steps, max_train_steps) without return types