code: def __getitem__(self, idx):
      path = self.local_images[idx]
      with bf.BlobFile(path, "rb") as f:
          pil_image = Image.open(f)
          pil_image.load()
  
      # We are not on a new enough PIL to support the `reducing_gap`
      # argument, which uses BOX downsampling at powers of two first.
      # Thus, we do it by hand to improve downsample quality.
      while min(*pil_image.size) >= 2 * self.resolution:
          pil_image = pil_image.resize(
              tuple(x // 2 for x in pil_image.size), resample=Image.BOX
          )
  
      scale = self.resolution / min(*pil_image.size)
      pil_image = pil_image.resize(
          tuple(round(x * scale) for x in pil_image.size),
          resample=Image.BICUBIC,
      )
  
      arr = np.array(pil_image.convert("RGB"))
      crop_y = (arr.shape[0] - self.resolution) // 2
      crop_x = (arr.shape[1] - self.resolution) // 2
      arr = arr[
          crop_y : crop_y + self.resolution,
          crop_x : crop_x + self.resolution,
      ]
      arr = arr.astype(np.float32) / 127.5 - 1
  
      out_dict = {}
      if self.local_classes is not None:
          out_dict["y"] = np.array(
              self.local_classes[idx], dtype=np.int64
          )
      return np.transpose(arr, [2, 0, 1]), out_dict
  
file_path: improved-diffusion/improved_diffusion/image_datasets.py
imports: from PIL import Image
  import blobfile
  from mpi4py import MPI
  import numpy
  from torch.utils.data import DataLoader, Dataset
label_desc: Use image node_id 3 for calling the ImageDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: np,  out_dict