def set_compare_estimator_and_feature_spec(self, estimator, feature_spec):
    """Sets a second model for inference as a TF Estimator.

    If you wish to compare the results of two models in WIT, use this method
    to setup the details of the second model.

    Instead of using TF Serving to host a model for WIT to query, WIT can
    directly use a TF Estimator object as the model to query. In order to
    accomplish this, a feature_spec must also be provided to parse the
    example protos for input into the estimator.

    Args:
      estimator: The TF Estimator which will be used for model inference.
      feature_spec: The feature_spec object which will be used for example
      parsing.

    Returns:
      self, in order to enabled method chaining.
    """
    # If custom function is set, remove it before setting estimator
    self.delete('compare_custom_predict_fn')

    self.store('compare_estimator_and_spec', {
      'estimator': estimator, 'feature_spec': feature_spec})
    self.set_compare_inference_address('estimator')
    # If no model name has been set, give a default
    if not self.has_compare_model_name():
      self.set_compare_model_name('2')
    return self
def set_compare_estimator_and_feature_spec ( self , estimator , feature_spec ) : self . delete ( 'compare_custom_predict_fn' ) self . store ( 'compare_estimator_and_spec' , { 'estimator' : estimator , 'feature_spec' : feature_spec } ) self . set_compare_inference_address ( 'estimator' ) if not self . has_compare_model_name ( ) : self . set_compare_model_name ( '2' ) return self
Sets a second model for inference as a TF Estimator.

    If you wish to compare the results of two models in WIT, use this method
    to setup the details of the second model.

    Instead of using TF Serving to host a model for WIT to query, WIT can
    directly use a TF Estimator object as the model to query. In order to
    accomplish this, a feature_spec must also be provided to parse the
    example protos for input into the estimator.

    Args:
      estimator: The TF Estimator which will be used for model inference.
      feature_spec: The feature_spec object which will be used for example
      parsing.

    Returns:
      self, in order to enabled method chaining.