code: def __init__(
      self,
      data_root,
      tokenizer,
      learnable_property="object",  # [object, style]
      size=512,
      repeats=100,
      interpolation="bicubic",
      flip_p=0.5,
      set="train",
      placeholder_token="*",
      center_crop=False,
  ):
      self.data_root = data_root
      self.tokenizer = tokenizer
      self.learnable_property = learnable_property
      self.size = size
      self.placeholder_token = placeholder_token
      self.center_crop = center_crop
      self.flip_p = flip_p
  
      self.image_paths = [
          os.path.join(self.data_root, file_path)
          for file_path in os.listdir(self.data_root)
      ]
  
      self.num_images = len(self.image_paths)
      self._length = self.num_images
  
      if set == "train":
          self._length = self.num_images * repeats
  
      self.interpolation = {
          "linear": PIL_INTERPOLATION["linear"],
          "bilinear": PIL_INTERPOLATION["bilinear"],
          "bicubic": PIL_INTERPOLATION["bicubic"],
          "lanczos": PIL_INTERPOLATION["lanczos"],
      }[interpolation]
  
      self.templates = (
          imagenet_style_templates_small
          if learnable_property == "style"
          else imagenet_templates_small
      )
      self.flip_transform = transforms.RandomHorizontalFlip(
          p=self.flip_p
      )
  
imports: import argparse
  import logging
  import math
  import os
  import random
  from pathlib import Path
  import jax
  import jax.numpy
  import numpy
  import optax
  import PIL
  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 packaging import version
  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 1 to create a new TextualInversionDataset object from inherited base classes: Dataset with example: obj = TextualInversionDataset(data_root, tokenizer, learnable_property, size, repeats, interpolation, flip_p, set, placeholder_token, center_crop)