code: def extract_embedding(args, data, predictor, column_names):
      features = []
      for per_name in column_names:
          per_features = torch.tensor(
              predictor.extract_embedding({per_name: data[per_name]})[
                  per_name
              ]
          ).cuda()
          features.append(per_features)
      features = torch.stack(features, dim=1).cuda()
      features = features.mean(dim=1)
      features /= features.norm(dim=-1, keepdim=True)
      return features
  
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 4 for calling a global function with example usage: extract_embedding(args, data, predictor, column_names) and returns: features