code: def fast_collate(memory_format, batch):
      imgs = [img[0] for img in batch]
      targets = torch.tensor(
          [target[1] for target in batch], dtype=torch.int64
      )
      w = imgs[0].size[0]
      h = imgs[0].size[1]
      tensor = torch.zeros(
          (len(imgs), 3, h, w), dtype=torch.uint8
      ).contiguous(memory_format=memory_format)
      for i, img in enumerate(imgs):
          nump_array = np.asarray(img, dtype=np.uint8)
          if nump_array.ndim < 3:
              nump_array = np.expand_dims(nump_array, axis=-1)
          nump_array = np.rollaxis(nump_array, 2)
  
          tensor[i] += torch.from_numpy(nump_array.copy())
  
      return tensor, targets
  
imports: import os
  import torch
  import numpy
  from PIL import Image
  from functools import partial
  from image_classification.autoaugment import AutoaugmentImageNetPolicy
  import torchvision.datasets
  import torchvision.transforms
label_desc: Use image node_id 4 for calling a global function with example usage: fast_collate(memory_format, batch) and returns: tensor,  targets