text: ###===###
  # Coder:        Nic K
  # Affiliation:  The Australian National University
  #===
  # Code purpose:
  #   Supporting script, prepares data loader
  
  import  random
  import  math
  import  numpy                   as      np
  
  import  torch
  import  torch.nn                as      nn
  import  torch.optim             as      optim
  import  torch.nn.functional     as      F
  
  import  torchvision
  import  torchvision.transforms  as      transforms
  from    torchvision             import  datasets, transforms
  
  ###===###
  def LoaderAz01(My_BS):
      kwargs = {'pin_memory': True}
  
      train_loader = \
          torch.utils.data.DataLoader(
              datasets.MNIST(
                  './data',
                  train = True, download = True,
                  transform = \
                      transforms.Compose([
                          transforms.ToTensor(),
                          transforms.Normalize((0.1307,), (0.3081,))
                          ])),
              batch_size = My_BS,
              shuffle = True,
              **kwargs)     
  
      test_loader = \
          torch.utils.data.DataLoader(
              datasets.MNIST(
                  './data',
                  train = False, download = True,
                  transform = \
                      transforms.Compose([
                          transforms.ToTensor(),
                          transforms.Normalize((0.1307,), (0.3081,))
                          ])),
              batch_size = 256,
              shuffle = True,
              **kwargs)
  
      return train_loader, test_loader
  