code: def process(self, training_example: dict) -> None:
      feature = training_example[self.field]
  
      speaker = training_example[self.speaker_field]
      if speaker in self.stats_dict:
          stats = self.stats_dict[speaker]
      elif self.fallback_to_default:
          stats = self.stats_dict["default"]
      else:
          raise ValueError(
              f"Statistics not found for speaker: {speaker}"
          )
  
      feature_mean = stats[self.key_mean]
      feature_std = stats[self.key_std]
  
      feature = (feature - feature_mean) / feature_std
  
      if self.mask_field:
          mask = training_example[self.mask_field]
          feature[~mask] = 0.0
  
      training_example[self.field] = feature
  
imports: import json
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import Optional
  import torch
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 2 for calling the MeanVarianceSpeakerNormalization obj's underlying member method code with example usage: obj.process(training_example) without return types