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.
class_docstr_tok: ["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 hashlib
  import itertools
  import math
  import os
  from pathlib import Path
  from typing import Optional
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from huggingface_hub import HfFolder, Repository, whoami
  from PIL import Image
  from torch.utils.data import Dataset
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, PretrainedConfig
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