parameters: (self, features, feature_columns, is_training)
docstring: Builds the model that can calculate the logits.
  
      Args:
        features: A mapping from feature columns to tensors.
        feature_columns: An iterable containing all the feature columns used
          by the model. All items in the set should be instances of
          classes derived from `FeatureColumn`.
        is_training: Set to True when training, False otherwise.
  
      Returns:
        The logits for this model.
docstring_summary: Builds the model that can calculate the logits.
docstring_tokens: Builds the model that can calculate the logits .
function: def build_model(self, features, feature_columns, is_training):
      """Builds the model that can calculate the logits.
  
      Args:
        features: A mapping from feature columns to tensors.
        feature_columns: An iterable containing all the feature columns used
          by the model. All items in the set should be instances of
          classes derived from `FeatureColumn`.
        is_training: Set to True when training, False otherwise.
  
      Returns:
        The logits for this model.
      """
      raise NotImplementedError
function_tokens: def build_model ( self , features , feature_columns , is_training ) : raise NotImplementedError