class_docstr: ImageNet dataset.
class_docstr_tok: ["ImageNet","dataset","."]
code: def __getitem__(self, index):
      if self.dummy_output is not None:
          return self.dummy_output
      # if the current image is corrupted, load a different image.
      for _ in range(self.num_retries):
          im = self.__load__(index)
          # Data corrupted, retry with a different image.
          if im is None:
              assert self.mode == "train", f"{index} failed loading"
              print(f"{index} failed. retry")
              index = random.randint(0, len(self._imdb) - 1)
          else:
              break
      # Retrieve the label
      label = self._imdb[index]["class"]
      if isinstance(im, list):
          if self.cfg.AUG.GEN_MASK_LOADER:
              dummy = torch.Tensor()
              label = torch.Tensor()
          else:
              label = [label for _ in range(len(im))]
              dummy = [torch.Tensor() for _ in range(len(im))]
          if self.cfg.DATA.DUMMY_LOAD:
              if self.dummy_output is None:
                  self.dummy_output = (im, label, index, dummy, {})
          return im, label, index, dummy, {}
      else:
          dummy = torch.Tensor()
          if self.cfg.DATA.DUMMY_LOAD:
              if self.dummy_output is None:
                  self.dummy_output = ([im], label, index, dummy, {})
          return [im], label, index, dummy, {}
  
imports: import json
  import numpy
  import os
  import random
  import re
  import torch
  import torch.utils.data
  from PIL import Image
  from torchvision import transforms
  import slowfast.datasets.transform
  import slowfast.utils.logging
  from slowfast.models.utils import calc_mvit_feature_geometry
  from slowfast.utils.env import pathmgr
  from .build import DATASET_REGISTRY
  from .transform import MaskingGenerator, transforms_imagenet_train
label_desc: Use image node_id 9 for calling the Imagenet obj's underlying member method code with example usage: obj.__getitem__(index) and returns: self, im,  label,  index,  dummy,  dict, list,  label,  index,  dummy,  dict