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: Predict on an SFrame or SArray of drawings, or on a single drawing.
  
  Parameters
  ----------
  data : SFrame | SArray | tc.Image
      The drawing(s) on which to perform drawing classification.
      If dataset is an SFrame, it must have a column with the same name
      as the feature column during training. Additional columns are
      ignored.
      If the data is a single drawing, it can be either of type tc.Image,
      in which case it is a bitmap-based drawing input,
      or of type list, in which case it is a stroke-based drawing input.
  
  output_type : {'probability', 'class', 'probability_vector'}, optional
      Form of the predictions which are one of:
  
      - 'class': Class prediction. For multi-class classification, this
        returns the class with maximum probability.
      - 'probability': Prediction probability associated with the True
        class (not applicable for multi-class classification)
      - 'probability_vector': Prediction probability associated with each
        class as a vector. Label ordering is dictated by the ``classes``
        member variable.
  
  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.
  
  verbose : bool, optional
      If True, prints prediction progress.
  
  Returns
  -------
  out : SArray
      An SArray with model predictions. Each element corresponds to
      a drawing and contains a single value corresponding to the
      predicted label. Each prediction will have type integer or string
      depending on the type of the classes the model was trained on.
      If `data` is a single drawing, the return value will be a single
      prediction.
  
  See Also
  --------
  evaluate
  
  Examples
  --------
  .. sourcecode:: python
  
      # Make predictions
      >>> pred = model.predict(data)
  
      # Print predictions, for a better overview
      >>> print(pred)
      dtype: int
      Rows: 10
      [3, 4, 3, 3, 4, 5, 8, 8, 8, 4]
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code: def predict(self, dataset, output_type="class"):
      """
      Predict on an SFrame or SArray of drawings, or on a single drawing.
  
      Parameters
      ----------
      data : SFrame | SArray | tc.Image
          The drawing(s) on which to perform drawing classification.
          If dataset is an SFrame, it must have a column with the same name
          as the feature column during training. Additional columns are
          ignored.
          If the data is a single drawing, it can be either of type tc.Image,
          in which case it is a bitmap-based drawing input,
          or of type list, in which case it is a stroke-based drawing input.
  
      output_type : {'probability', 'class', 'probability_vector'}, optional
          Form of the predictions which are one of:
  
          - 'class': Class prediction. For multi-class classification, this
            returns the class with maximum probability.
          - 'probability': Prediction probability associated with the True
            class (not applicable for multi-class classification)
          - 'probability_vector': Prediction probability associated with each
            class as a vector. Label ordering is dictated by the ``classes``
            member variable.
  
      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.
  
      verbose : bool, optional
          If True, prints prediction progress.
  
      Returns
      -------
      out : SArray
          An SArray with model predictions. Each element corresponds to
          a drawing and contains a single value corresponding to the
          predicted label. Each prediction will have type integer or string
          depending on the type of the classes the model was trained on.
          If `data` is a single drawing, the return value will be a single
          prediction.
  
      See Also
      --------
      evaluate
  
      Examples
      --------
      .. sourcecode:: python
  
          # Make predictions
          >>> pred = model.predict(data)
  
          # Print predictions, for a better overview
          >>> print(pred)
          dtype: int
          Rows: 10
          [3, 4, 3, 3, 4, 5, 8, 8, 8, 4]
      """
      if isinstance(dataset, _tc.SArray):
          dataset = _tc.SFrame({self.feature: dataset})
      return self.__proxy__.predict(dataset, output_type)
  
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 7 for calling the DrawingClassifier obj's underlying member method code with example usage: obj.predict(dataset, output_type) and returns: self