code: def transpose_batch(batch):
      """
      ### Transpose batch
  
      `DataLoader` collects the batches on the first dimension.
      We need to transpose it to be sequence first.
      """
  
      transposed_data = list(zip(*batch))
      # Stack the batch along the second dimension `dim=1`
      src = torch.stack(transposed_data[0], dim=1)
      tgt = torch.stack(transposed_data[1], dim=1)
  
      return src, tgt
  
imports: from typing import Callable
  import torch
  import torch.nn
  from torch.utils.data import DataLoader, RandomSampler
  from labml import lab, monit, logger, tracker
  from labml.configs import option
  from labml.logger import Text
  from labml_helpers.datasets.text import TextDataset, SequentialDataLoader, SequentialUnBatchedDataset, TextFileDataset
  from labml_helpers.device import DeviceConfigs
  from labml_helpers.metrics.accuracy import Accuracy
  from labml_helpers.module import Module
  from labml_helpers.train_valid import TrainValidConfigs, hook_model_outputs, BatchIndex
  from labml_nn.optimizers.configs import OptimizerConfigs
label_desc: Use image node_id 9 for calling a global function with example usage: transpose_batch(batch) and returns: src,  tgt