class_docstr: A trained model using C++ implementation that is ready to use for classification or export to
  CoreML.
  
  This model should not be constructed directly.
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docstr: Return a classification, for each ``prediction_window`` examples in the
  ``dataset``, using the trained activity classification 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 and session id used for model training, but
      does not require a target column. Additional columns are ignored.
  
  output_frequency : {'per_row', 'per_window'}, optional
      The frequency of the predictions which is one of:
  
      - 'per_row': Each prediction is returned ``prediction_window`` times.
      - 'per_window': Return a single prediction for each
        ``prediction_window`` rows in ``dataset`` per ``session_id``.
  
  Returns
  -------
  out : SFrame
      An SFrame with model predictions i.e class labels and probabilities.
  
  See Also
  ----------
  create, evaluate, predict
  
  Examples
  ----------
  >>> classes = model.classify(data)
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code: def classify(self, dataset, output_frequency="per_row"):
      """
      Return a classification, for each ``prediction_window`` examples in the
      ``dataset``, using the trained activity classification 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 and session id used for model training, but
          does not require a target column. Additional columns are ignored.
  
      output_frequency : {'per_row', 'per_window'}, optional
          The frequency of the predictions which is one of:
  
          - 'per_row': Each prediction is returned ``prediction_window`` times.
          - 'per_window': Return a single prediction for each
            ``prediction_window`` rows in ``dataset`` per ``session_id``.
  
      Returns
      -------
      out : SFrame
          An SFrame with model predictions i.e class labels and probabilities.
  
      See Also
      ----------
      create, evaluate, predict
  
      Examples
      ----------
      >>> classes = model.classify(data)
      """
      return self.__proxy__.classify(dataset, output_frequency)
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  import time
  import six
  from turicreate import SFrame
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits import _coreml_utils
  import turicreate.toolkits._feature_engineering._internal_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate._deps.minimal_package import _minimal_package_import_check
  from turicreate.toolkits._model import Model
label_desc: Use image node_id 10 for calling the ActivityClassifier obj's underlying member method code with example usage: obj.classify(dataset, output_frequency) and returns: self