code: def fast_collate(batch, memory_format):
      """Based on fast_collate from the APEX example
      https://github.com/NVIDIA/apex/blob/5b5d41034b506591a316c308c3d2cd14d5187e23/examples/imagenet/main_amp.py#L265
      """
      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)
      return tensor, targets
  
imports: import argparse
  import os
  import shutil
  import time
  import math
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.backends.cudnn
  import torch.distributed
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import numpy
  from torch.nn.parallel import DistributedDataParallel
  import torchvision.transforms
  import torchvision.datasets
  import torchvision.models
label_desc: Use image node_id 1 for calling a global function with example usage: fast_collate(batch, memory_format) and returns: tensor,  targets