code: def show_batch(dl):
      for images, masks in dl:
          fig1, ax1 = plt.subplots(figsize=(24, 24))
          ax1.set_xticks([])
          ax1.set_yticks([])
          denorm_images = denormalize(images)
          ax1.imshow(
              make_grid(denorm_images[:13], nrow=13)
              .permute(1, 2, 0)
              .clamp(0, 1)
          )
          fig2, ax2 = plt.subplots(figsize=(24, 24))
          ax2.set_xticks([])
          ax2.set_yticks([])
          ax2.imshow(
              make_grid(masks[:13], nrow=13)
              .permute(1, 2, 0)
              .clamp(0, 1)
          )
          break
  
imports: from __future__ import print_function
  from sklearn.model_selection import train_test_split
  import matplotlib.pyplot
  import albumentations
  import pandas
  import argparse
  import glob
  import os
  import torch
  import torch.nn
  import torch.optim
  import torch.utils.data
  from torchvision.utils import make_grid
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.pytorch import Estimator
  from Unet import UNet
  from dataset import *
label_desc: Use image node_id 6 for calling a global function with example usage: show_batch(dl) without return types