class_docstr: A dataset to prepare the instance and class images with the prompts for fine-tuning the model.
  It pre-processes the images and the tokenizes prompts.
code: def __getitem__(self, index):
      example = {}
      instance_image = Image.open(
          self.instance_images_path[index % self.num_instance_images]
      )
      if not instance_image.mode == "RGB":
          instance_image = instance_image.convert("RGB")
      example["instance_images"] = self.image_transforms(instance_image)
      example["instance_prompt_ids"] = self.tokenizer(
          self.instance_prompt,
          padding="do_not_pad",
          truncation=True,
          max_length=self.tokenizer.model_max_length,
      ).input_ids
  
      if self.class_data_root:
          class_image = Image.open(
              self.class_images_path[index % self.num_class_images]
          )
          if not class_image.mode == "RGB":
              class_image = class_image.convert("RGB")
          example["class_images"] = self.image_transforms(class_image)
          example["class_prompt_ids"] = self.tokenizer(
              self.class_prompt,
              padding="do_not_pad",
              truncation=True,
              max_length=self.tokenizer.model_max_length,
          ).input_ids
  
      return example
  
imports: import argparse
  import logging
  import math
  import os
  from pathlib import Path
  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 create_repo, upload_folder
  from huggingface_hub.utils import insecure_hashlib
  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 DreamBoothDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: example