code: def print_metrics(self) -> None:
      super(NASMaskedSeq2SeqJointMetrics, self).print_metrics()
  
      if self.model_num_param:
          print(f"\nNumber of Parameters {self.model_num_param}")
      if self.norm_prod_model_param_frame_accuracy is not None:
          print(
              f"Normalized product of model parameter and frame accuracy {self.norm_prod_model_param_frame_accuracy}"
          )
  
file_path: pytext/pytext/metrics/mask_metrics.py
imports: from typing import Dict, List, NamedTuple, Optional, Sequence
  from pytext.metrics import ClassificationMetrics, compute_classification_metrics, LabelPrediction
  from pytext.metrics.intent_slot_metrics import compute_all_metrics, compute_frame_accuracy, FrameAccuraciesByDepth, FramePredictionPair, IntentSlotMetrics, Node
  from sklearn.metrics import accuracy_score
label_desc: Use image node_id 2 for calling the NASMaskedSeq2SeqJointMetrics obj's underlying member method code with example usage: obj.print_metrics() without return types