code: def dali_data_iter(
      batch_size: int,
      rec_file: str,
      idx_file: str,
      num_threads: int,
      initial_fill=32768,
      random_shuffle=True,
      prefetch_queue_depth=1,
      local_rank=0,
      name="reader",
      mean=(127.5, 127.5, 127.5),
      std=(127.5, 127.5, 127.5),
      dali_aug=False,
  ):
      """
      Parameters:
      ----------
      initial_fill: int
          Size of the buffer that is used for shuffling. If random_shuffle is False, this parameter is ignored.
  
      """
      rank: int = distributed.get_rank()
      world_size: int = distributed.get_world_size()
      import nvidia.dali.fn as fn
      import nvidia.dali.types as types
      from nvidia.dali.pipeline import Pipeline
      from nvidia.dali.plugin.pytorch import DALIClassificationIterator
  
      def dali_random_resize(img, resize_size, image_size=112):
          img = fn.resize(
              img, resize_x=resize_size, resize_y=resize_size
          )
          img = fn.resize(img, size=(image_size, image_size))
          return img
  
      def dali_random_gaussian_blur(img, window_size):
          img = fn.gaussian_blur(img, window_size=window_size * 2 + 1)
          return img
  
      def dali_random_gray(img, prob_gray):
          saturate = fn.random.coin_flip(probability=1 - prob_gray)
          saturate = fn.cast(saturate, dtype=types.FLOAT)
          img = fn.hsv(img, saturation=saturate)
          return img
  
      def dali_random_hsv(img, hue, saturation):
          img = fn.hsv(img, hue=hue, saturation=saturation)
          return img
  
      def multiplexing(condition, true_case, false_case):
          neg_condition = condition ^ True
          return condition * true_case + neg_condition * false_case
  
      condition_resize = fn.random.coin_flip(probability=0.1)
      size_resize = fn.random.uniform(
          range=(int(112 * 0.5), int(112 * 0.8)), dtype=types.FLOAT
      )
      condition_blur = fn.random.coin_flip(probability=0.2)
      window_size_blur = fn.random.uniform(
          range=(1, 2), dtype=types.INT32
      )
      condition_flip = fn.random.coin_flip(probability=0.5)
      condition_hsv = fn.random.coin_flip(probability=0.2)
      hsv_hue = fn.random.uniform(range=(0.0, 20.0), dtype=types.FLOAT)
      hsv_saturation = fn.random.uniform(
          range=(1.0, 1.2), dtype=types.FLOAT
      )
  
      pipe = Pipeline(
          batch_size=batch_size,
          num_threads=num_threads,
          device_id=local_rank,
          prefetch_queue_depth=prefetch_queue_depth,
      )
      condition_flip = fn.random.coin_flip(probability=0.5)
      with pipe:
          jpegs, labels = fn.readers.mxnet(
              path=rec_file,
              index_path=idx_file,
              initial_fill=initial_fill,
              num_shards=world_size,
              shard_id=rank,
              random_shuffle=random_shuffle,
              pad_last_batch=False,
              name=name,
          )
          images = fn.decoders.image(
              jpegs, device="mixed", output_type=types.RGB
          )
          if dali_aug:
              images = fn.cast(images, dtype=types.UINT8)
              images = multiplexing(
                  condition_resize,
                  dali_random_resize(
                      images, size_resize, image_size=112
                  ),
                  images,
              )
              images = multiplexing(
                  condition_blur,
                  dali_random_gaussian_blur(images, window_size_blur),
                  images,
              )
              images = multiplexing(
                  condition_hsv,
                  dali_random_hsv(images, hsv_hue, hsv_saturation),
                  images,
              )
              images = dali_random_gray(images, 0.1)
  
          images = fn.crop_mirror_normalize(
              images,
              dtype=types.FLOAT,
              mean=mean,
              std=std,
              mirror=condition_flip,
          )
          pipe.set_outputs(images, labels)
      pipe.build()
      return DALIWarper(
          DALIClassificationIterator(
              pipelines=[pipe],
              reader_name=name,
          )
      )
  
imports: import numbers
  import os
  import queue
  import threading
  from typing import Iterable
  import mxnet
  import numpy
  import torch
  from functools import partial
  from torch import distributed
  from torch.utils.data import DataLoader, Dataset
  from torchvision import transforms
  from torchvision.datasets import ImageFolder
  from utils.utils_distributed_sampler import DistributedSampler
  from utils.utils_distributed_sampler import get_dist_info, worker_init_fn
label_desc: Use image node_id 2 for calling a global function with example usage: dali_data_iter(batch_size, rec_file, idx_file, num_threads, initial_fill, random_shuffle, prefetch_queue_depth, local_rank, name, mean, std, dali_aug) and returns: DALIWarper, img, img, img, img, unknown