docstr: Return predictions for ``dataset``, using the trained model.
  
  Parameters
  ----------
  dataset : SFrame
      dataset of new observations. Must include columns with the same
      names as the features used for model training, but does not require
      a target column. Additional columns are ignored.
  
  output_type : {'class', 'probability_vector'}, optional
      Form of the predictions which are one of:
  
      - 'probability_vector': Prediction probability associated with each
        class as a vector. The probability of the first class (sorted
        alphanumerically by name of the class in the training set) is in
        position 0 of the vector, the second in position 1 and so on.
      - 'class': Class prediction. For multi-class classification, this
        returns the class with maximum probability.
  
  Returns
  -------
  out : SArray
      An SArray with model predictions.
  
  See Also
  ----------
  create, evaluate, classify
  
  
  Examples
  --------
  >>> import turicreate as tc
  >>> dataset = tc.SFrame({'rating': [1, 5], 'text': ['hate it', 'love it']})
  >>> m = tc.text_classifier.create(dataset, 'rating', features=['text'])
  >>> m.predict(dataset)
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code: def predict(self, dataset, output_type="class"):
      """
      Return predictions for ``dataset``, using the trained model.
  
      Parameters
      ----------
      dataset : SFrame
          dataset of new observations. Must include columns with the same
          names as the features used for model training, but does not require
          a target column. Additional columns are ignored.
  
      output_type : {'class', 'probability_vector'}, optional
          Form of the predictions which are one of:
  
          - 'probability_vector': Prediction probability associated with each
            class as a vector. The probability of the first class (sorted
            alphanumerically by name of the class in the training set) is in
            position 0 of the vector, the second in position 1 and so on.
          - 'class': Class prediction. For multi-class classification, this
            returns the class with maximum probability.
  
      Returns
      -------
      out : SArray
          An SArray with model predictions.
  
      See Also
      ----------
      create, evaluate, classify
  
  
      Examples
      --------
      >>> import turicreate as tc
      >>> dataset = tc.SFrame({'rating': [1, 5], 'text': ['hate it', 'love it']})
      >>> m = tc.text_classifier.create(dataset, 'rating', features=['text'])
      >>> m.predict(dataset)
  
      """
      m = self.__proxy__["classifier"]
      target = self.__proxy__["target"]
      f = _BOW_FEATURE_EXTRACTOR
      return m.predict(f(dataset, target), output_type=output_type)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
  from turicreate.toolkits._model import CustomModel
  from turicreate.toolkits._model import PythonProxy
  from turicreate.toolkits._internal_utils import _toolkit_repr_print
  from turicreate.toolkits import text_analytics
label_desc: Use image node_id 6 for calling the TextClassifier obj's underlying member method code with example usage: obj.predict(dataset, output_type) and returns: m