class_docstr: ImageNet dataset.
class_docstr_tok: ["ImageNet","dataset","."]
code: def __load__(self, index):
      try:
          # Load the image
          im_path = self._imdb[index]["im_path"]
          # Prepare the image for training / testing
          if self.cfg.AUG.ENABLE:
              if self.cfg.AUG.GEN_MASK_LOADER:
                  return self._prepare_im_masked(im_path)
              elif self.mode == "train" and self.cfg.AUG.NUM_SAMPLE > 1:
                  im = []
                  for _ in range(self.cfg.AUG.NUM_SAMPLE):
                      crop = self._prepare_im_tf(im_path)
                      im.append(crop)
                  return im
              else:
                  im = self._prepare_im_tf(im_path)
                  return im
          else:
              im = self._prepare_im_res(im_path)
              return im
      except Exception:
          return None
  
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 8 for calling the Imagenet obj's underlying member method code with example usage: obj.__load__(index) and returns: im, None, self, im, im