code: def create(model_config, batch_size, normalize=True, num_workers=0, augmentations=None):
      
      path = model_config.data_dir('mnist')
  
      train_dataset = datasets.MNIST(path, train=True, download=True)
      test_dataset = datasets.MNIST(path, train=False, download=True)
  
      augmentations = [ToArray()] + (augmentations if augmentations is not None else [])
  
      if normalize:
          train_data = train_dataset.train_data
          mean_value = (train_data.double() / 255).mean().item()
          std_value = (train_data.double() / 255).std().item()
  
          augmentations.append(Normalize(mean=mean_value, std=std_value, tags=['train', 'val']))
  
      augmentations.append(ToTensor())
  
      return TrainingData(
          train_dataset,
          test_dataset,
          num_workers=num_workers,
          batch_size=batch_size,
          augmentations=augmentations
      )
docs: Create a MNIST dataset, normalized