code: def imshow(inp=None, title=None):
      """
      Visualize a few training images
      so as to understand the data augmentations.
      """
      if inp is None:
          train_loader, test_loader, class_names = load_dataset(
              data_dir, batch_size=4
          )
  
          inputs, classes = next(
              iter(train_loader)
          )  # Get a batch of training data
          inp = torchvision.utils.make_grid(
              inputs
          )  # Make a grid from batch
          title = [class_names[x] for x in classes]
  
      inp = inp.numpy().transpose((1, 2, 0))
      mean = np.array([0.485, 0.456, 0.406])
      std = np.array([0.229, 0.224, 0.225])
      inp = std * inp + mean
      inp = np.clip(inp, 0, 1)
      plt.imshow(inp)
      if title is not None:
          plt.title(title)
      plt.pause(0.001)
  
file_path: BigDL/python/orca/tutorial/pytorch/transfer_learning/train.py
imports: import os
  import numpy
  import matplotlib.pyplot
  import torch
  import torch.nn
  import torch.optim
  from torch.optim import lr_scheduler
  import torchvision
  from torchvision import datasets, transforms
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.pytorch import Estimator
  from bigdl.orca.learn.metrics import Accuracy
label_desc: Use image node_id 4 for calling a global function with example usage: imshow(inp, title) without return types