code: def __getitem__(self, i):
      example = self.base[i]
      image = Image.open(example["file_path_"])
  
      if not image.mode == "RGB":
          image = image.convert("RGB")
  
      image = np.array(image).astype(np.uint8)
  
      min_side_len = min(image.shape[:2])
      crop_side_len = min_side_len * np.random.uniform(
          self.min_crop_f, self.max_crop_f, size=None
      )
      crop_side_len = int(crop_side_len)
  
      if self.center_crop:
          self.cropper = albumentations.CenterCrop(
              height=crop_side_len, width=crop_side_len
          )
  
      else:
          self.cropper = albumentations.RandomCrop(
              height=crop_side_len, width=crop_side_len
          )
  
      image = self.cropper(image=image)["image"]
      image = self.image_rescaler(image=image)["image"]
  
      if self.pil_interpolation:
          image_pil = PIL.Image.fromarray(image)
          LR_image = self.degradation_process(image_pil)
          LR_image = np.array(LR_image).astype(np.uint8)
  
      else:
          LR_image = self.degradation_process(image=image)["image"]
  
      example["image"] = (image / 127.5 - 1.0).astype(np.float32)
      example["LR_image"] = (LR_image / 127.5 - 1.0).astype(np.float32)
  
      return example
  
imports: import glob
  import os
  import pickle
  import shutil
  import tarfile
  from functools import partial
  import albumentations
  import cv2
  import numpy
  import PIL
  import taming.data.utils
  import torchvision.transforms.functional
  import yaml
  from ldm.modules.image_degradation import degradation_fn_bsr, degradation_fn_bsr_light
  from omegaconf import OmegaConf
  from PIL import Image
  from taming.data.imagenet import ImagePaths, download, give_synsets_from_indices, retrieve, str_to_indices
  from torch.utils.data import Dataset, Subset
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling the ImageNetSR obj's underlying member method code with example usage: obj.__getitem__(i) and returns: example