code: def train_and_eval(trainer, data, num_eval_batches=100, max_epochs=1):
      train_dataloader = DataLoader(
          data[:-num_eval_batches], collate_fn=lambda x: x[0]
      )
      eval_data = data[-num_eval_batches:]
  
      # disable logging in tests
      pl_trainer = pl.Trainer(max_epochs=max_epochs, logger=False)
      pl_trainer.fit(trainer, train_dataloader)
  
      total_loss = 0
      for i, batch in enumerate(eval_data):
          loss = trainer.validation_step(batch, batch_idx=i)
          total_loss += loss
      return total_loss / num_eval_batches
  
imports: import logging
  import unittest
  import pytorch_lightning
  import torch
  from reagent.core import parameters, types
  from reagent.models.synthetic_reward import NGramConvolutionalNetwork, NGramFullyConnectedNetwork, SequenceSyntheticRewardNet, SingleStepSyntheticRewardNet, SyntheticRewardNet, TransformerSyntheticRewardNet
  from reagent.optimizer.union import classes, Optimizer__Union
  from reagent.reporting.reward_network_reporter import RewardNetworkReporter
  from reagent.training import RewardNetTrainer
  from reagent.training.reward_network_trainer import LossFunction
  from torch.utils.data import DataLoader
label_desc: Use image node_id 3 for calling a global function with example usage: train_and_eval(trainer, data, num_eval_batches, max_epochs) and returns: unknown