code: def prepare(self):
      random_uuid = uuid.uuid4()
      cache_dir = os.environ.get("HF_HOME")
      if not cache_dir:
          cache_dir = os.path.join(
              os.path.expanduser("~"), ".cache", "huggingface"
          )
      data_dir = os.path.join(cache_dir, "autotrain", str(random_uuid))
  
      if self.valid_data:
          shutil.copytree(
              self.train_data, os.path.join(data_dir, "train")
          )
          shutil.copytree(
              self.valid_data, os.path.join(data_dir, "validation")
          )
  
          dataset = load_dataset("imagefolder", data_dir=data_dir)
          dataset = dataset.rename_columns(
              {"image": "autotrain_image", "label": "autotrain_label"}
          )
          if self.local:
              dataset.save_to_disk(
                  f"{self.project_name}/autotrain-data"
              )
          else:
              dataset.push_to_hub(
                  f"{self.username}/autotrain-data-{self.project_name}",
                  private=True,
                  token=self.token,
              )
  
      else:
          subfolders = [
              f.path for f in os.scandir(self.train_data) if f.is_dir()
          ]
  
          image_filenames = []
          subfolder_names = []
  
          for subfolder in subfolders:
              for filename in os.listdir(subfolder):
                  if filename.endswith(("jpeg", "png", "jpg")):
                      image_filenames.append(filename)
                      subfolder_names.append(
                          os.path.basename(subfolder)
                      )
  
          df = pd.DataFrame(
              {
                  "image_filename": image_filenames,
                  "subfolder": subfolder_names,
              }
          )
          train_df, valid_df = self.split(df)
  
          for row in train_df.itertuples():
              os.makedirs(
                  os.path.join(data_dir, "train", row.subfolder),
                  exist_ok=True,
              )
              shutil.copy(
                  os.path.join(
                      self.train_data, row.subfolder, row.image_filename
                  ),
                  os.path.join(
                      data_dir,
                      "train",
                      row.subfolder,
                      row.image_filename,
                  ),
              )
  
          for row in valid_df.itertuples():
              os.makedirs(
                  os.path.join(data_dir, "validation", row.subfolder),
                  exist_ok=True,
              )
              shutil.copy(
                  os.path.join(
                      self.train_data, row.subfolder, row.image_filename
                  ),
                  os.path.join(
                      data_dir,
                      "validation",
                      row.subfolder,
                      row.image_filename,
                  ),
              )
  
          dataset = load_dataset("imagefolder", data_dir=data_dir)
          dataset = dataset.rename_columns(
              {"image": "autotrain_image", "label": "autotrain_label"}
          )
          if self.local:
              dataset.save_to_disk(
                  f"{self.project_name}/autotrain-data"
              )
          else:
              dataset.push_to_hub(
                  f"{self.username}/autotrain-data-{self.project_name}",
                  private=True,
                  token=self.token,
              )
  
      if self.local:
          return f"{self.project_name}/autotrain-data"
      return f"{self.username}/autotrain-data-{self.project_name}"
  
file_path: autotrain-advanced/src/autotrain/preprocessor/vision.py
imports: import os
  import shutil
  import uuid
  from dataclasses import dataclass
  from typing import Optional
  import pandas
  from datasets import load_dataset
  from sklearn.model_selection import train_test_split
  from autotrain import logger
label_desc: Use image node_id 3 for calling the ImageClassificationPreprocessor obj's underlying member method code with example usage: obj.prepare() and returns: self, str, self, self, str