docstr: Export the model in Core ML format.
  
  Parameters
  ----------
  filename: str
    A valid filename where the model can be saved.
  
  Examples
  --------
  >>> model.export_coreml("MyTextMessageClassifier.mlmodel")
  >>>
  >>> from coremltools.models import MLModel
  >>> coreml_model = MLModel("MyTextMessageClassifier.mlmodel")
  >>>
  >>> test_input = tc.SArray(["Hi! How are you?"])
  >>> bag_of_words = tc.text_analytics.count_words(test_input)
  >>>
  >>> # "text" is the input column name
  >>> coreml_model.predict({"text": bag_of_words[0]})
docstr_tok: ["Export","the","model","in","Core","ML","format",".","Parameters","--","--","--","--","--","filename",":","str","A","valid","filename","where","the","model","can","be","saved",".","Examples","--","--","--","--",">",">",">","model.export_coreml","(","``","MyTextMessageClassifier.mlmodel","''",")",">",">",">",">",">",">","from","coremltools.models","import","MLModel",">",">",">","coreml_model","=","MLModel","(","``","MyTextMessageClassifier.mlmodel","''",")",">",">",">",">",">",">","test_input","=","tc.SArray","(","[","``","Hi","!","How","are","you","?","''","]",")",">",">",">","bag_of_words","=","tc.text_analytics.count_words","(","test_input",")",">",">",">",">",">",">","#","``","text","''","is","the","input","column","name",">",">",">","coreml_model.predict","(","{","``","text","''",":","bag_of_words","[","0","]","}",")"]
code: 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("MyTextMessageClassifier.mlmodel")
      >>>
      >>> from coremltools.models import MLModel
      >>> coreml_model = MLModel("MyTextMessageClassifier.mlmodel")
      >>>
      >>> test_input = tc.SArray(["Hi! How are you?"])
      >>> bag_of_words = tc.text_analytics.count_words(test_input)
      >>>
      >>> # "text" is the input column name
      >>> coreml_model.predict({"text": bag_of_words[0]})
      """
      from turicreate.extensions import (
          _logistic_classifier_export_as_model_asset,
      )
      from turicreate.toolkits import _coreml_utils
  
      display_name = "text 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
      )
  
      model = self.__proxy__["classifier"].__proxy__
      _logistic_classifier_export_as_model_asset(
          model, filename, context
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
  from turicreate.toolkits._model import CustomModel
  from turicreate.toolkits._model import PythonProxy
  from turicreate.toolkits._internal_utils import _toolkit_repr_print
  from turicreate.toolkits import text_analytics
label_desc: Use image node_id 13 for calling the TextClassifier obj's underlying member method code with example usage: obj.export_coreml(filename) without return types