code: def predict(self, sentence: Sentence):
      if self.estimator == TEXT_CLASSIFIER_ESTIMATOR.FAST_TEXT:
          values, scores = self.ft.predict(sentence.text)
          labels = []
          for value, score in zip(values, scores):
              value = value.replace("__label__", "")
              label = Label(value, score)
              labels.append(label)
          sentence.add_labels(labels)
  
      if self.estimator == TEXT_CLASSIFIER_ESTIMATOR.SVC:
          text = sentence.text
          X = self.x_transformer.transform([text])
          y = self.svc.predict(X)
          y = self.y_transformer.inverse_transform(y)
          sentence.add_labels(y)
  
      if self.estimator == TEXT_CLASSIFIER_ESTIMATOR.PIPELINE:
          text = sentence.text
          y = self.pipeline.predict([text])
          if self.multilabel:
              y = self.y_encoder.inverse_transform(y)
              y = list(y[0])
          else:
              y = list(y)
          sentence.add_labels(y)
  
file_path: underthesea/underthesea/models/text_classifier.py
imports: from enum import Enum
  from os.path import join
  import json
  import joblib
  from sklearn.preprocessing import MultiLabelBinarizer
  import warnings
  from underthesea.corpus.data import Sentence, Label
label_desc: Use image node_id 3 for calling the TextClassifier obj's underlying member method code with example usage: obj.predict(sentence) without return types