code: def _sample_and_train_torch_distributed(worker: RolloutWorker):
      # This function is applied remotely on each rollout worker.
      config: AlgorithmConfig = worker.config
  
      # Generate a sample.
      start = time.perf_counter()
      batch = worker.sample()
      sample_time = time.perf_counter() - start
      expected_batch_size = (
          config.get_rollout_fragment_length()
          * config.num_envs_per_worker
      )
      assert batch.count == expected_batch_size, (
          "Batch size possibly out of sync between workers, expected:",
          expected_batch_size,
          "got:",
          batch.count,
      )
  
      # Perform n minibatch SGD update(s) on the worker itself.
      start = time.perf_counter()
      info = do_minibatch_sgd(
          batch,
          worker.policy_map,
          worker,
          config.num_sgd_iter,
          config.sgd_minibatch_size,
          [Postprocessing.ADVANTAGES],
      )
      learn_on_batch_time = time.perf_counter() - start
      return {
          "info": info,
          "env_steps": batch.env_steps(),
          "agent_steps": batch.agent_steps(),
          "sample_time": sample_time,
          "learn_on_batch_time": learn_on_batch_time,
      }
  
imports: import logging
  import time
  from typing import Optional
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.ppo import PPO, PPOConfig
  from ray.rllib.evaluation.postprocessing import Postprocessing
  from ray.rllib.evaluation.rollout_worker import RolloutWorker
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.metrics import LEARN_ON_BATCH_TIMER, NUM_AGENT_STEPS_SAMPLED, NUM_AGENT_STEPS_TRAINED, NUM_ENV_STEPS_SAMPLED, NUM_ENV_STEPS_TRAINED, SAMPLE_TIMER
  from ray.rllib.utils.metrics.learner_info import LearnerInfoBuilder
  from ray.rllib.utils.sgd import do_minibatch_sgd
  from ray.rllib.utils.typing import ResultDict
label_desc: Use image node_id 4 for calling the DDPPO obj's underlying member method code with example usage: obj._sample_and_train_torch_distributed(worker) and returns: dict