code: def create_for_trainer(
      cls,
      trainer,
      env: EnvWrapper,
      agent: Agent,
      replay_buffer: ReplayBuffer,
      batch_size: int,
      training_frequency: int = 1,
      num_episodes: Optional[int] = None,
      max_steps: Optional[int] = None,
      post_episode_callback: Optional[Callable] = None,
      trainer_preprocessor=None,
      replay_buffer_inserter=None,
      device=None,
  ):
      device = device or torch.device("cpu")
      if trainer_preprocessor is None:
          trainer_preprocessor = (
              make_replay_buffer_trainer_preprocessor(
                  trainer, device, env
              )
          )
  
      if replay_buffer_inserter is None:
          replay_buffer_inserter = make_replay_buffer_inserter(env)
  
      return cls(
          env=env,
          agent=agent,
          replay_buffer=replay_buffer,
          batch_size=batch_size,
          training_frequency=training_frequency,
          num_episodes=num_episodes,
          max_steps=max_steps,
          post_episode_callback=post_episode_callback,
          trainer_preprocessor=trainer_preprocessor,
          replay_buffer_inserter=replay_buffer_inserter,
      )
  
file_path: ReAgent/reagent/gym/datasets/replay_buffer_dataset.py
imports: import logging
  from typing import Callable, Optional
  import torch
  from reagent.gym.agents.agent import Agent
  from reagent.gym.envs import EnvWrapper
  from reagent.gym.preprocessors import make_replay_buffer_inserter, make_replay_buffer_trainer_preprocessor
  from reagent.gym.types import Trajectory, Transition
  from reagent.replay_memory.circular_replay_buffer import ReplayBuffer
label_desc: Use image node_id 2 for calling the ReplayBufferDataset obj's underlying member method code with example usage: obj.create_for_trainer(cls, trainer, env, agent, replay_buffer, batch_size, training_frequency, num_episodes, max_steps, post_episode_callback, trainer_preprocessor, replay_buffer_inserter, device) and returns: cls