docstr: Return a classification, for each example in the ``dataset``, using the
  trained model. The output SFrame contains predictions as both class
  labels as well as probabilities that the predicted value is the
  associated label.
  
  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.
  
  Returns
  -------
  out : SFrame
      An SFrame with model predictions i.e class labels and probabilities.
  
  See Also
  ----------
  create, evaluate, predict
  
  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'])
  >>> output = m.classify(dataset)
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code: def classify(self, dataset):
      """
      Return a classification, for each example in the ``dataset``, using the
      trained model. The output SFrame contains predictions as both class
      labels as well as probabilities that the predicted value is the
      associated label.
  
      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.
  
      Returns
      -------
      out : SFrame
          An SFrame with model predictions i.e class labels and probabilities.
  
      See Also
      ----------
      create, evaluate, predict
  
      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'])
      >>> output = m.classify(dataset)
  
      """
      m = self.__proxy__["classifier"]
      target = self.__proxy__["target"]
      f = _BOW_FEATURE_EXTRACTOR
      return m.classify(f(dataset, target))
  
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 7 for calling the TextClassifier obj's underlying member method code with example usage: obj.classify(dataset) and returns: m