class_docstr: A trained model that is ready to use for sound classification or to export to CoreML.
  
  This model should not be constructed directly.
  
  See Also
  ----------
  create
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docstr: Return the classification for each examples in the ``dataset``.
  The output SFrame contains predicted class labels and its probability.
  
  Parameters
  ----------
  dataset : SFrame | SArray | dict
      The audio data to be classified.
      If dataset is an SFrame, it must have a column with the same name as
      the feature used for model training, but does not require a target
      column. Additional columns are ignored.
  
  verbose : bool, optional
      If True, prints progress updates and model details.
  
  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, both class labels and probabilities.
  
  See Also
  ----------
  create, evaluate, predict
  
  Examples
  ----------
  >>> classes = model.classify(data)
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code: def classify(self, dataset, verbose=True, batch_size=64):
      """
      Return the classification for each examples in the ``dataset``.
      The output SFrame contains predicted class labels and its probability.
  
      Parameters
      ----------
      dataset : SFrame | SArray | dict
          The audio data to be classified.
          If dataset is an SFrame, it must have a column with the same name as
          the feature used for model training, but does not require a target
          column. Additional columns are ignored.
  
      verbose : bool, optional
          If True, prints progress updates and model details.
  
      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, both class labels and probabilities.
  
      See Also
      ----------
      create, evaluate, predict
  
      Examples
      ----------
      >>> classes = model.classify(data)
      """
      prob_vector = self.predict(
          dataset,
          output_type="probability_vector",
          verbose=verbose,
          batch_size=batch_size,
      )
      id_to_label = self._id_to_class_label
  
      return _tc.SFrame(
          {
              "class": prob_vector.apply(
                  lambda v: id_to_label[_np.argmax(v)]
              ),
              "probability": prob_vector.apply(_np.max),
          }
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import logging
  from math import ceil
  import numpy
  import six
  import turicreate
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits._model import CustomModel
  from turicreate.toolkits._model import PythonProxy
  from turicreate.toolkits import _coreml_utils
  from turicreate._deps.minimal_package import _minimal_package_import_check
label_desc: Use image node_id 9 for calling the SoundClassifier obj's underlying member method code with example usage: obj.classify(dataset, verbose, batch_size) and returns: _tc