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()
      pil_image = pil_image.convert("RGB")
  
      if self.random_crop:
          arr = random_crop_arr(pil_image, self.resolution)
      else:
          arr = center_crop_arr(pil_image, self.resolution)
  
      if self.random_flip and random.random() < 0.5:
          arr = arr[:, ::-1]
  
      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: consistency_models/cm/image_datasets.py
imports: import math
  import random
  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