text: from PIL import Image
  import os
  import random
  import sys
  
  import numpy as np
  
  # %matplotlib inline
  from matplotlib import pyplot as plt
  import habitat
  from habitat.core.logging import logger
  from habitat.core.registry import registry
  from habitat.sims.habitat_simulator.actions import HabitatSimActions
  from habitat.tasks.nav.nav import NavigationTask
  from habitat_baselines.common.baseline_registry import baseline_registry
  from habitat_baselines.config.default import get_config as get_baselines_config
  from habitat import make_dataset
  
  def get_replica_config(config):
      r""" Updates the config to replicate scenes.
      Updates the config to include replicated scenes to support multiple workers
      when there are insufficient workers.
      :param config: Habitat.config
      :return: Habitat.config
      """
      # Determine the number of scenes in this dataset.
      dataset = make_dataset(config.TASK_CONFIG.DATASET.TYPE)
      scenes = config.TASK_CONFIG.DATASET.CONTENT_SCENES
      if "*" in config.TASK_CONFIG.DATASET.CONTENT_SCENES:
          scenes = dataset.get_scenes_to_load(config.TASK_CONFIG.DATASET)
  
      if len(scenes) >= config.NUM_ENVIRONMENTS:
          # Fallback to default handling in habitat.
          return config
  
      assert config.NUM_ENVIRONMENTS % len(scenes) == 0, "Number of processes must be divisible by number of scenes."
  
      # Determine number of replicas.
      num_replicas = config.NUM_ENVIRONMENTS // len(scenes)
  
      # Update config.
      config.defrost()
      config.TASK_CONFIG.DATASET.CONTENT_SCENES = scenes * num_replicas
      config.freeze()
  
      return config
  
  if __name__ == "__main__":
      # config = get_baselines_config(
      #     "./habitat_baselines/config/pointnav/ppo_pointnav_example.yaml"
      # )
      config = get_baselines_config(
          "./habitat_baselines/config/roomnav/ppo_roomnav.yaml"
      )
      #config = get_replica_config(config)
      trainer_init = baseline_registry.get_trainer(config.TRAINER_NAME)
      trainer = trainer_init(config)
      #trainer.train()
      trainer.eval()
  
  