code: def _convert_corpus(self, corpus: Corpus, multilabel=False):
      X_train = [s.text for s in corpus.train]
      X_dev = [s.text for s in corpus.dev]
      X_test = [s.text for s in corpus.test]
      if multilabel:
          y_train = [
              [label.value for label in s.labels] for s in corpus.train
          ]
          y_dev = [
              [label.value for label in s.labels] for s in corpus.dev
          ]
          y_test = [
              [label.value for label in s.labels] for s in corpus.test
          ]
      else:
          y_train = [s.labels[0].value for s in corpus.train]
          y_dev = [s.labels[0].value for s in corpus.dev]
          y_test = [s.labels[0].value for s in corpus.test]
      return (X_train, y_train), (X_dev, y_dev), (X_test, y_test)
  
file_path: underthesea/underthesea/trainers/classifier_trainer.py
imports: import json
  import warnings
  from underthesea.corpus import Corpus
  from underthesea.models.text_classifier import TextClassifier, TEXT_CLASSIFIER_ESTIMATOR
  from sklearn.metrics import f1_score
  from sklearn.preprocessing import LabelEncoder
  from os.path import join
  import joblib
label_desc: Use image node_id 3 for calling the ClassifierTrainer obj's underlying member method code with example usage: obj._convert_corpus(corpus, multilabel) and returns: X_train,  y_train,  X_dev,  y_dev,  X_test,  y_test