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 gc
  import hashlib
  import itertools
  import logging
  import math
  import os
  import threading
  import warnings
  from contextlib import nullcontext
  from pathlib import Path
  from typing import Optional
  import datasets
  import diffusers
  import numpy
  import psutil
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import transformers
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import set_seed
  from diffusers import AutoencoderKL, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.utils import check_min_version
  from diffusers.utils.import_utils import is_xformers_available
  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
  from peft import get_peft_model
  from peft.tuners.oft.config import OFTConfig
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