parameters: (self,
                            features,
                            mode,
                            labels=None,
                            train_op_fn=None,
                            logits=None,
                            logits_input=None,
                            scope=None)
docstring: See `Head`.
docstring_summary: See `Head`.
docstring_tokens: See Head .
function: def create_model_fn_ops(self,
                            features,
                            mode,
                            labels=None,
                            train_op_fn=None,
                            logits=None,
                            logits_input=None,
                            scope=None):
      """See `Head`."""
      with variable_scope.variable_scope(
          scope,
          default_name=self.head_name or "multi_label_head",
          values=(tuple(six.itervalues(features)) +
                  (labels, logits, logits_input))):
        labels = self._transform_labels(mode=mode, labels=labels)
        logits = _logits(logits_input, logits, self.logits_dimension)
        return _create_model_fn_ops(
            features=features,
            mode=mode,
            loss_fn=self._loss_fn,
            logits_to_predictions_fn=self._logits_to_predictions,
            metrics_fn=self._metrics,
            create_output_alternatives_fn=_classification_output_alternatives(
                self.head_name, self._problem_type),
            labels=labels,
            train_op_fn=train_op_fn,
            logits=logits,
            logits_dimension=self.logits_dimension,
            head_name=self.head_name,
            weight_column_name=self.weight_column_name,
            enable_centered_bias=self._enable_centered_bias)
function_tokens: def create_model_fn_ops ( self , features , mode , labels = None , train_op_fn = None , logits = None , logits_input = None , scope = None ) : with variable_scope . variable_scope ( scope , default_name = self . head_name or "multi_label_head" , values = ( tuple ( six . itervalues ( features ) ) + ( labels , logits , logits_input ) ) ) : labels = self . _transform_labels ( mode = mode , labels = labels ) logits = _logits ( logits_input , logits , self . logits_dimension ) return _create_model_fn_ops ( features = features , mode = mode , loss_fn = self . _loss_fn , logits_to_predictions_fn = self . _logits_to_predictions , metrics_fn = self . _metrics , create_output_alternatives_fn = _classification_output_alternatives ( self . head_name , self . _problem_type ) , labels = labels , train_op_fn = train_op_fn , logits = logits , logits_dimension = self . logits_dimension , head_name = self . head_name , weight_column_name = self . weight_column_name , enable_centered_bias = self . _enable_centered_bias )