code: def matplotlib_imshow(img, one_channel=False):
      if one_channel:
          img = img.mean(dim=0)
      img = img / 2 + 0.5  # unnormalize
      npimg = img.numpy()
      if one_channel:
          plt.imshow(npimg, cmap="Greys")
      else:
          plt.imshow(np.transpose(npimg, (1, 2, 0)))
  
imports: from __future__ import print_function
  import argparse
  import numpy
  import matplotlib.pyplot
  import torch
  import torchvision
  import torchvision.transforms
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.utils.tensorboard import SummaryWriter
  from zoo.orca import init_orca_context, stop_orca_context
  from zoo.orca.learn.pytorch import Estimator
  from zoo.orca.learn.metrics import Accuracy
  from zoo.orca.learn.trigger import EveryEpoch
label_desc: Use image node_id 3 for calling a global function with example usage: matplotlib_imshow(img, one_channel) without return types