old_contents: import argparse
  import pickle
  
  from rlkit.core import logger
  from rlkit.samplers.rollout_functions import multitask_rollout
  from rlkit.torch import pytorch_util as ptu
  from rlkit.envs.vae_wrapper import VAEWrappedEnv
  
  
  def simulate_policy(args):
      data = pickle.load(open(args.file, "rb"))
      policy = data['evaluation/policy']
      env = data['evaluation/env']
      print("Policy and environment loaded")
      if args.gpu:
          ptu.set_gpu_mode(True)
          policy.to(ptu.device)
      if isinstance(env, VAEWrappedEnv) and hasattr(env, 'mode'):
          env.mode(args.mode)
      if args.enable_render or hasattr(env, 'enable_render'):
          # some environments need to be reconfigured for visualization
          env.enable_render()
      paths = []
      while True:
          paths.append(multitask_rollout(
              env,
              policy,
              max_path_length=args.H,
              render=not args.hide,
              observation_key='observation',
              desired_goal_key='desired_goal',
          ))
          if hasattr(env, "log_diagnostics"):
              env.log_diagnostics(paths)
          if hasattr(env, "get_diagnostics"):
              for k, v in env.get_diagnostics(paths).items():
                  logger.record_tabular(k, v)
          logger.dump_tabular()
  
  
  if __name__ == "__main__":
  
      parser = argparse.ArgumentParser()
      parser.add_argument('file', type=str,
                          help='path to the snapshot file')
      parser.add_argument('--H', type=int, default=300,
                          help='Max length of rollout')
      parser.add_argument('--speedup', type=float, default=10,
                          help='Speedup')
      parser.add_argument('--mode', default='video_env', type=str,
                          help='env mode')
      parser.add_argument('--gpu', action='store_true')
      parser.add_argument('--enable_render', action='store_true')
      parser.add_argument('--hide', action='store_true')
      args = parser.parse_args()
  
      simulate_policy(args)
  
new_contents: import argparse
  import torch
  
  from rlkit.core import logger
  from rlkit.samplers.rollout_functions import multitask_rollout
  from rlkit.torch import pytorch_util as ptu
  from rlkit.envs.vae_wrapper import VAEWrappedEnv
  
  
  def simulate_policy(args):
      data = torch.load(args.file)
      policy = data['evaluation/policy']
      env = data['evaluation/env']
      print("Policy and environment loaded")
      if args.gpu:
          ptu.set_gpu_mode(True)
          policy.to(ptu.device)
      if isinstance(env, VAEWrappedEnv) and hasattr(env, 'mode'):
          env.mode(args.mode)
      if args.enable_render or hasattr(env, 'enable_render'):
          # some environments need to be reconfigured for visualization
          env.enable_render()
      paths = []
      while True:
          paths.append(multitask_rollout(
              env,
              policy,
              max_path_length=args.H,
              render=not args.hide,
              observation_key='observation',
              desired_goal_key='desired_goal',
          ))
          if hasattr(env, "log_diagnostics"):
              env.log_diagnostics(paths)
          if hasattr(env, "get_diagnostics"):
              for k, v in env.get_diagnostics(paths).items():
                  logger.record_tabular(k, v)
          logger.dump_tabular()
  
  
  if __name__ == "__main__":
  
      parser = argparse.ArgumentParser()
      parser.add_argument('file', type=str,
                          help='path to the snapshot file')
      parser.add_argument('--H', type=int, default=300,
                          help='Max length of rollout')
      parser.add_argument('--speedup', type=float, default=10,
                          help='Speedup')
      parser.add_argument('--mode', default='video_env', type=str,
                          help='env mode')
      parser.add_argument('--gpu', action='store_true')
      parser.add_argument('--enable_render', action='store_true')
      parser.add_argument('--hide', action='store_true')
      args = parser.parse_args()
  
      simulate_policy(args)
  
subject: fix loading in run_goal_conditioned_policy.py