class_docstr: A simple dataset to prepare the prompts to generate class images on multiple GPUs.
code: def __getitem__(self, index):
      example = {}
      example["prompt"] = self.prompt
      example["index"] = index
      return example
  
imports: import argparse
  import hashlib
  import logging
  import math
  import os
  from pathlib import Path
  from typing import Optional
  import jax
  import jax.numpy
  import numpy
  import optax
  import torch
  import torch.utils.checkpoint
  import transformers
  from flax import jax_utils
  from flax.training import train_state
  from flax.training.common_utils import shard
  from huggingface_hub import HfFolder, Repository, create_repo, whoami
  from jax.experimental.compilation_cache import compilation_cache
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import CLIPImageProcessor, CLIPTokenizer, FlaxCLIPTextModel, set_seed
  from diffusers import FlaxAutoencoderKL, FlaxDDPMScheduler, FlaxPNDMScheduler, FlaxStableDiffusionPipeline, FlaxUNet2DConditionModel
  from diffusers.pipelines.stable_diffusion import FlaxStableDiffusionSafetyChecker
  from diffusers.utils import check_min_version
label_desc: Use image node_id 3 for calling the PromptDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: example