code: def gen_wrapper(dalipipeline, num_classes, one_hot, memory_format):
      for data in dalipipeline:
          if memory_format == torch.channels_last:
              # If we requested the data in channels_last form, utilize the fact that DALI
              # can return it as NHWC. The network expects NCHW shape with NHWC internal memory,
              # so we can keep the memory and just create a view with appropriate shape and
              # strides reflacting that memory layouyt
              shape = data[0]["data"].shape
              stride = data[0]["data"].stride()
  
              # permute shape and stride from NHWC to NCHW
              def nhwc_to_nchw(t):
                  return t[0], t[3], t[1], t[2]
  
              input = torch.as_strided(
                  data[0]["data"],
                  size=nhwc_to_nchw(shape),
                  stride=nhwc_to_nchw(stride),
              )
          else:
              input = data[0]["data"].contiguous(
                  memory_format=memory_format
              )
          target = torch.reshape(data[0]["label"], [-1]).cuda().long()
          if one_hot:
              target = expand(num_classes, torch.float, target)
          yield input, target
      dalipipeline.reset()
  
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 1 for calling the DALIWrapper obj's underlying member method code with example usage: obj.gen_wrapper(dalipipeline, num_classes, one_hot, memory_format) and returns: t,  t,  t,  t