code: def get_file_df_binary_semantic_seg(need_test_gt=False):
      data_dir = download_binary_semantic_seg_sample_dataset()
      train_img_files = get_file_paths(
          os.path.join(data_dir, "train/ISIC-2017_Train")
      )
      train_gt_files = get_file_paths(
          os.path.join(
              data_dir, "train/ISIC-2017_Training_Part1_GroundTruth"
          )
      )
      val_img_files = get_file_paths(
          os.path.join(data_dir, "val/ISIC-2017_Val")
      )
      val_gt_files = get_file_paths(
          os.path.join(
              data_dir, "val/ISIC-2017_Validation_Part1_GroundTruth"
          )
      )
      test_img_files = get_file_paths(
          os.path.join(data_dir, "test/ISIC-2017_Test")
      )
      test_gt_files = get_file_paths(
          os.path.join(
              data_dir, "test/ISIC-2017_Test_v2_Part1_GroundTruth"
          )
      )
  
      train_df = pd.DataFrame(
          {"image": train_img_files, "label": train_gt_files}
      )
      val_df = pd.DataFrame(
          {"image": val_img_files, "label": val_gt_files}
      )
      if need_test_gt:
          test_df = pd.DataFrame(
              {"image": test_img_files, "label": test_gt_files}
          )
      else:
          test_df = pd.DataFrame({"image": test_img_files})
      return train_df, val_df, test_df
  
imports: import os
  import shutil
  import uuid
  import numpy
  import numpy.testing
  import pandas
  import pytest
  import torch
  from autogluon.core.utils.loaders import load_zip
  from autogluon.multimodal import MultiModalPredictor
label_desc: Use image node_id 5 for calling a global function with example usage: get_file_df_binary_semantic_seg(need_test_gt) and returns: train_df,  val_df,  test_df