parameters: (self, filename)
docstring: Export the model in Core ML format.
  
          Parameters
          ----------
          filename: str
            A valid filename where the model can be saved.
  
          Examples
          --------
          >>> model.export_coreml("MyModel.mlmodel")
docstring_summary: Export the model in Core ML format.
docstring_tokens: Export the model in Core ML format .
function: def export_coreml(self, filename):
          """
          Export the model in Core ML format.
  
          Parameters
          ----------
          filename: str
            A valid filename where the model can be saved.
  
          Examples
          --------
          >>> model.export_coreml("MyModel.mlmodel")
          """
          from turicreate.extensions import _logistic_classifier_export_as_model_asset
          from turicreate.toolkits import _coreml_utils
  
          display_name = "logistic classifier"
          short_description = _coreml_utils._mlmodel_short_description(display_name)
          context = {
              "class": self.__class__.__name__,
              "short_description": short_description,
          }
          context["user_defined"] = _coreml_utils._get_model_metadata(
              self.__class__.__name__, None
          )
          _logistic_classifier_export_as_model_asset(self.__proxy__, filename, context)
function_tokens: def export_coreml ( self , filename ) : from turicreate . extensions import _logistic_classifier_export_as_model_asset from turicreate . toolkits import _coreml_utils display_name = "logistic classifier" short_description = _coreml_utils . _mlmodel_short_description ( display_name ) context = { "class" : self . __class__ . __name__ , "short_description" : short_description , } context [ "user_defined" ] = _coreml_utils . _get_model_metadata ( self . __class__ . __name__ , None ) _logistic_classifier_export_as_model_asset ( self . __proxy__ , filename , context )