code: def create_synthetic_data(
      num_batches_train: int, num_batches_eval: int
  ) -> rlt.BehavioralCloningModelInput:
      train_batches = get_dummy_batch(
          action_type=SyntheticType.ACTION_TYPE.value,
          num_batches=num_batches_train,
      )
      train_dataloader = DataLoader(
          train_batches, collate_fn=lambda x: x[0]
      )
  
      eval_batches = get_dummy_batch(
          action_type=SyntheticType.ACTION_TYPE.value,
          num_batches=num_batches_eval,
      )
      eval_dataloader = DataLoader(
          eval_batches, collate_fn=lambda x: x[0]
      )
  
      return train_dataloader, eval_dataloader
  
imports: import logging
  import unittest
  from enum import Enum
  import pytorch_lightning
  import torch
  from pytorch_lightning import seed_everything
  from reagent.core import types
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.optimizer.union import classes, Optimizer__Union
  from reagent.training.behavioral_cloning_trainer import BehavioralCloningTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling a global function with example usage: create_synthetic_data(num_batches_train, num_batches_eval) and returns: train_dataloader,  eval_dataloader