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.
docstr: Return top-k predictions for the ``dataset``, using the trained model.
  Predictions are returned as an SFrame with three columns: `id`,
  `class`, and `probability` or `rank`, depending on the ``output_type``
  parameter.
  
  Parameters
  ----------
  dataset : SFrame | SArray | turicreate.Image
      Drawings to be classified.
      If dataset is an SFrame, it 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 : {'probability', 'rank'}, optional
      Choose the return type of the prediction:
  
      - `probability`: Probability associated with each label in the
                       prediction.
      - `rank`       : Rank associated with each label in the prediction.
  
  k : int, optional
      Number of classes to return for each input example.
  
  batch_size : int, optional
      If you are getting memory errors, try decreasing this value. If you
      have a powerful computer, increasing this value may improve
      performance.
  
  Returns
  -------
  out : SFrame
      An SFrame with model predictions.
  
  See Also
  --------
  predict, evaluate
  
  Examples
  --------
  >>> pred = m.predict_topk(validation_data, k=3)
  >>> print(pred)
  +----+-------+-------------------+
  | id | class |   probability     |
  +----+-------+-------------------+
  | 0  |   4   |   0.995623886585  |
  | 0  |   9   |  0.0038311756216  |
  | 0  |   7   | 0.000301006948575 |
  | 1  |   1   |   0.928708016872  |
  | 1  |   3   |  0.0440889261663  |
  | 1  |   2   |  0.0176190119237  |
  | 2  |   3   |   0.996967732906  |
  | 2  |   2   |  0.00151345680933 |
  | 2  |   7   | 0.000637513934635 |
  | 3  |   1   |   0.998070061207  |
  | .. |  ...  |        ...        |
  +----+-------+-------------------+
  [35688 rows x 3 columns]
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code: def predict_topk(self, dataset, output_type="probability", k=3):
      """
      Return top-k predictions for the ``dataset``, using the trained model.
      Predictions are returned as an SFrame with three columns: `id`,
      `class`, and `probability` or `rank`, depending on the ``output_type``
      parameter.
  
      Parameters
      ----------
      dataset : SFrame | SArray | turicreate.Image
          Drawings to be classified.
          If dataset is an SFrame, it 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 : {'probability', 'rank'}, optional
          Choose the return type of the prediction:
  
          - `probability`: Probability associated with each label in the
                           prediction.
          - `rank`       : Rank associated with each label in the prediction.
  
      k : int, optional
          Number of classes to return for each input example.
  
      batch_size : int, optional
          If you are getting memory errors, try decreasing this value. If you
          have a powerful computer, increasing this value may improve
          performance.
  
      Returns
      -------
      out : SFrame
          An SFrame with model predictions.
  
      See Also
      --------
      predict, evaluate
  
      Examples
      --------
      >>> pred = m.predict_topk(validation_data, k=3)
      >>> print(pred)
      +----+-------+-------------------+
      | id | class |   probability     |
      +----+-------+-------------------+
      | 0  |   4   |   0.995623886585  |
      | 0  |   9   |  0.0038311756216  |
      | 0  |   7   | 0.000301006948575 |
      | 1  |   1   |   0.928708016872  |
      | 1  |   3   |  0.0440889261663  |
      | 1  |   2   |  0.0176190119237  |
      | 2  |   3   |   0.996967732906  |
      | 2  |   2   |  0.00151345680933 |
      | 2  |   7   | 0.000637513934635 |
      | 3  |   1   |   0.998070061207  |
      | .. |  ...  |        ...        |
      +----+-------+-------------------+
      [35688 rows x 3 columns]
      """
      if isinstance(dataset, _tc.SArray):
          dataset = _tc.SFrame({self.feature: dataset})
      return self.__proxy__.predict_topk(dataset, output_type, k)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits import _coreml_utils
  from turicreate.toolkits._model import Model
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate._deps.minimal_package import _minimal_package_import_check
  from ..image_classifier._evaluation import Evaluation
  from ..None import _pre_trained_models
label_desc: Use image node_id 8 for calling the DrawingClassifier obj's underlying member method code with example usage: obj.predict_topk(dataset, output_type, k) and returns: self