code: def extract_val_test(args, predictor, val_df, test_df):
      val_features = extract_embedding(
          args, val_df, predictor, args.column_names
      )
      test_features = extract_embedding(
          args, test_df, predictor, args.column_names
      )
      val_labels = torch.tensor(val_df[args.label_column]).cuda()
      test_labels = torch.tensor(test_df[args.label_column]).cuda()
      return val_features, val_labels, test_features, test_labels
  
imports: import torch
  import pandas
  from tqdm import tqdm
  import torch.nn.functional
  import torch.nn
  from autogluon.multimodal import MultiModalPredictor
  from datasets import list_datasets, load_dataset
  from setfit import sample_dataset
  from imagedatasets import build_dataset
  from torch.utils.data import Dataset
label_desc: Use image node_id 7 for calling a global function with example usage: extract_val_test(args, predictor, val_df, test_df) and returns: val_features,  val_labels,  test_features,  test_labels