code: def __init__(
      self,
      train_shards_path_or_url: Union[str, List[str]],
      num_train_examples: int,
      per_gpu_batch_size: int,
      global_batch_size: int,
      num_workers: int,
      resolution: int = 512,
      interpolation_type: str = "bilinear",
      shuffle_buffer_size: int = 1000,
      pin_memory: bool = False,
      persistent_workers: bool = False,
  ):
      if not isinstance(train_shards_path_or_url, str):
          train_shards_path_or_url = [
              list(braceexpand(urls))
              for urls in train_shards_path_or_url
          ]
          # flatten list using itertools
          train_shards_path_or_url = list(
              itertools.chain.from_iterable(train_shards_path_or_url)
          )
  
      interpolation_mode = resolve_interpolation_mode(
          interpolation_type
      )
  
      def transform(example):
          # resize image
          image = example["image"]
          image = TF.resize(
              image, resolution, interpolation=interpolation_mode
          )
  
          # get crop coordinates and crop image
          c_top, c_left, _, _ = transforms.RandomCrop.get_params(
              image, output_size=(resolution, resolution)
          )
          image = TF.crop(image, c_top, c_left, resolution, resolution)
          image = TF.to_tensor(image)
          image = TF.normalize(image, [0.5], [0.5])
  
          example["image"] = image
          return example
  
      processing_pipeline = [
          wds.decode("pil", handler=wds.ignore_and_continue),
          wds.rename(
              image="jpg;png;jpeg;webp",
              text="text;txt;caption",
              handler=wds.warn_and_continue,
          ),
          wds.map(filter_keys({"image", "text"})),
          wds.map(transform),
          wds.to_tuple("image", "text"),
      ]
  
      # Create train dataset and loader
      pipeline = [
          wds.ResampledShards(train_shards_path_or_url),
          tarfile_to_samples_nothrow,
          wds.shuffle(shuffle_buffer_size),
          *processing_pipeline,
          wds.batched(
              per_gpu_batch_size,
              partial=False,
              collation_fn=default_collate,
          ),
      ]
  
      num_worker_batches = math.ceil(
          num_train_examples / (global_batch_size * num_workers)
      )  # per dataloader worker
      num_batches = num_worker_batches * num_workers
      num_samples = num_batches * global_batch_size
  
      # each worker is iterating over this
      self._train_dataset = wds.DataPipeline(*pipeline).with_epoch(
          num_worker_batches
      )
      self._train_dataloader = wds.WebLoader(
          self._train_dataset,
          batch_size=None,
          shuffle=False,
          num_workers=num_workers,
          pin_memory=pin_memory,
          persistent_workers=persistent_workers,
      )
      # add meta-data to dataloader instance for convenience
      self._train_dataloader.num_batches = num_batches
      self._train_dataloader.num_samples = num_samples
  
imports: import argparse
  import functools
  import gc
  import itertools
  import json
  import logging
  import math
  import os
  import random
  import shutil
  from pathlib import Path
  from typing import List, Union
  import accelerate
  import numpy
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  import torchvision.transforms.functional
  import transformers
  import webdataset
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration, set_seed
  from braceexpand import braceexpand
  from huggingface_hub import create_repo, upload_folder
  from packaging import version
  from peft import LoraConfig, get_peft_model, get_peft_model_state_dict
  from torch.utils.data import default_collate
  from torchvision import transforms
  from tqdm.auto import tqdm
  from transformers import AutoTokenizer, CLIPTextModel, PretrainedConfig
  from webdataset.tariterators import base_plus_ext, tar_file_expander, url_opener, valid_sample
  import diffusers
  from diffusers import AutoencoderKL, DDPMScheduler, LCMScheduler, StableDiffusionPipeline, UNet2DConditionModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import resolve_interpolation_mode
  from diffusers.utils import check_min_version, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 1 to create a new SDText2ImageDataset object  with example: obj = SDText2ImageDataset(train_shards_path_or_url, num_train_examples, per_gpu_batch_size, global_batch_size, num_workers, resolution, interpolation_type, shuffle_buffer_size, pin_memory, persistent_workers)