code: def __init__(
      self,
      resolution,
      image_paths,
      classes=None,
      shard=0,
      num_shards=1,
      random_crop=False,
      random_flip=True,
  ):
      super().__init__()
      self.resolution = resolution
      self.local_images = image_paths[shard:][::num_shards]
      self.local_classes = (
          None if classes is None else classes[shard:][::num_shards]
      )
      self.random_crop = random_crop
      self.random_flip = random_flip
  
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 1 to create a new ImageDataset object from inherited base classes: Dataset with example: obj = ImageDataset(resolution, image_paths, classes, shard, num_shards, random_crop, random_flip)