code: def __init__(self, mean, std):
      super(Normalization, self).__init__()
      # .view the mean and std to make them [C x 1 x 1] so that they can
      # directly work with image Tensor of shape [B x C x H x W].
      # B is batch size. C is number of channels. H is height and W is width.
      self.mean = torch.tensor(mean).view(-1, 1, 1)
      self.std = torch.tensor(std).view(-1, 1, 1)
  
imports: from __future__ import print_function
  import os
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from PIL import Image
  import matplotlib.pyplot
  import torchvision.transforms
  import torchvision.models
  import copy
  from clearml import Task
label_desc: Use image node_id 1 to create a new Normalization object from inherited base classes: nn with example: obj = Normalization(mean, std)