code: def train_mdnrnn(
      env: EnvWrapper,
      trainer: MDNRNNTrainer,
      trainer_preprocessor,
      num_train_transitions: int,
      seq_len: int,
      batch_size: int,
      num_train_epochs: int,
      # for optional validation
      test_replay_buffer=None,
  ):
      train_replay_buffer = ReplayBuffer(
          replay_capacity=num_train_transitions,
          batch_size=batch_size,
          stack_size=seq_len,
          return_everything_as_stack=True,
      )
      random_policy = make_random_policy_for_env(env)
      agent = Agent.create_for_env(env, policy=random_policy)
      fill_replay_buffer(
          env, train_replay_buffer, num_train_transitions, agent
      )
      num_batch_per_epoch = train_replay_buffer.size // batch_size
  
      logger.info("Made RBs, starting to train now!")
      optimizer = trainer.configure_optimizers()[0]
      for _ in range(num_train_epochs):
          for i in range(num_batch_per_epoch):
              batch = train_replay_buffer.sample_transition_batch(
                  batch_size=batch_size
              )
              preprocessed_batch = trainer_preprocessor(batch)
              loss = next(trainer.train_step_gen(preprocessed_batch, i))
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
  
          # validation
          if test_replay_buffer is not None:
              with torch.no_grad():
                  trainer.memory_network.mdnrnn.eval()
                  test_batch = (
                      test_replay_buffer.sample_transition_batch(
                          batch_size=batch_size
                      )
                  )
                  preprocessed_test_batch = trainer_preprocessor(
                      test_batch
                  )
                  valid_losses = trainer.get_loss(
                      preprocessed_test_batch
                  )
                  trainer.memory_network.mdnrnn.train()
      return trainer
  
imports: import logging
  import os
  import unittest
  from typing import Dict, List, Optional
  import gym
  import numpy
  import reagent.core.types
  import torch
  from reagent.evaluation.world_model_evaluator import FeatureImportanceEvaluator, FeatureSensitivityEvaluator
  from reagent.gym.agents.agent import Agent
  from reagent.gym.envs import EnvWrapper, Gym
  from reagent.gym.envs.pomdp.state_embed_env import StateEmbedEnvironment
  from reagent.gym.policies.random_policies import make_random_policy_for_env
  from reagent.gym.preprocessors import make_replay_buffer_trainer_preprocessor
  from reagent.gym.runners.gymrunner import evaluate_for_n_episodes
  from reagent.gym.utils import build_normalizer, fill_replay_buffer
  from reagent.model_managers.union import ModelManager__Union
  from reagent.models.world_model import MemoryNetwork
  from reagent.replay_memory.circular_replay_buffer import ReplayBuffer
  from reagent.test.base.horizon_test_base import HorizonTestBase
  from reagent.training.world_model.mdnrnn_trainer import MDNRNNTrainer
  from reagent.workflow.types import RewardOptions
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: train_mdnrnn(env, trainer, trainer_preprocessor, num_train_transitions, seq_len, batch_size, num_train_epochs, test_replay_buffer) and returns: trainer