code: def preprocess_data_sklearn(train, test):
      vectorizer = TfidfVectorizer(stop_words="english")
  
      y_train = list_to_csr(
          train["labels"], shape=(len(train), NUM_LABELS)
      )
      y_test = list_to_csr(
          test["labels"], shape=(len(test), NUM_LABELS)
      )
  
      ds_train = SklearnDataset.from_arrays(
          train["text"], y_train, vectorizer, train=True
      )
      ds_test = SklearnDataset.from_arrays(
          test["text"], y_test, vectorizer, train=False
      )
  
      return ds_train, ds_test
  
imports: from sklearn.feature_extraction.text import TfidfVectorizer
  from small_text import SklearnDataset, list_to_csr
  from examplecode.data.dataset_go_emotions import get_go_emotions_dataset
label_desc: Use image node_id 2 for calling a global function with example usage: preprocess_data_sklearn(train, test) and returns: ds_train,  ds_test