class_docstr: A trained model that is ready to use for classification or export to CoreML.
  
  This model should not be constructed directly.
class_docstr_tok: ["A","trained","model","that","is","ready","to","use","for","classification","or","export","to","CoreML",".","This","model","should","not","be","constructed","directly","."]
docstr: Takes input and returns tuple of the input in canonical form (SFrame)
  along with an unpack callback function that can be applied to
  prediction results to "undo" the canonization.
docstr_tok: ["Takes","input","and","returns","tuple","of","the","input","in","canonical","form","(","SFrame",")","along","with","an","unpack","callback","function","that","can","be","applied","to","prediction","results","to","``","undo","''","the","canonization","."]
code: def _canonize_input(self, dataset):
      """
      Takes input and returns tuple of the input in canonical form (SFrame)
      along with an unpack callback function that can be applied to
      prediction results to "undo" the canonization.
      """
      from array import array
  
      unpack = lambda x: x
      if isinstance(dataset, _tc.SArray):
          dataset = _tc.SFrame({self.feature: dataset})
      elif isinstance(dataset, (_tc.Image, array)):
          dataset = _tc.SFrame({self.feature: [dataset]})
          unpack = lambda x: x[0]
  
      return dataset, unpack
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  import time
  from turicreate.toolkits._model import CustomModel
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits import _coreml_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits._model import PythonProxy
  from turicreate._deps.minimal_package import _minimal_package_import_check
  from .._internal_utils import _mac_ver
  from ..None import _pre_trained_models
  from ..None import _image_feature_extractor
  from ..image_analysis import image_analysis
  from ._evaluation import Evaluation
label_desc: Use image node_id 9 for calling the ImageClassifier obj's underlying member method code with example usage: obj._canonize_input(dataset) and returns: dataset,  unpack