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 __len__(self):
      return self._length
  
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 2 for calling the DreamBoothDataset obj's underlying member method code with example usage: obj.__len__() and returns: self