code: def main(_):
      # Configure the environment factory with requested task.
      make_environment = functools.partial(
          helpers.make_environment,
          domain_name=_DOMAIN.value,
          task_name=_TASK.value,
          from_pixels=True,
          frames_to_stack=3,
          flatten_stack=True,
          num_action_repeats=2,
      )
  
      # Construct the program.
      program_builder = dmpo.DistributedDistributionalMPO(
          make_environment,
          make_networks,
          target_policy_update_period=100,
          max_actor_steps=_MAX_ACTOR_STEPS.value,
          num_actors=4,
          samples_per_insert=256,
          n_step=3,  # Reduce the n-step to account for action-repeat.
          observation_augmentation=image_augmentation.pad_and_crop,
      )
  
      # Launch experiment.
      lp.launch(programs=program_builder.build())
  
file_path: acme/examples/tf/control_suite/lp_dmpo_pixels_drqv2.py
imports: import functools
  from typing import Dict, Sequence
  from absl import app
  from absl import flags
  from acme import specs
  from acme.agents.tf import dmpo
  from acme.datasets import image_augmentation
  import helpers
  from acme.tf import networks
  import launchpad
  import numpy
  import sonnet
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: main(_) without return types