code: def test_mean_variance_normalization(self):
      field = "test_feat"
      filename = "stats.json"
      stat_dict = {
          "default": {"test_feat_mean": 1.5, "test_feat_std": 0.5}
      }
  
      input_tensor = torch.tensor([0.0, 1.5, 2.0], dtype=torch.float32)
      expected_tensor = torch.tensor(
          [-3.0, 0.0, 1.0], dtype=torch.float32
      )
      training_example = {field: input_tensor}
  
      with self._write_test_dict(
          stat_dict, filename=filename
      ) as stat_dict_filepath:
          processor = MeanVarianceNormalization(
              field, stats_path=stat_dict_filepath, mask_field=None
          )
          processor.process(training_example)
  
      output_tensor = training_example[field]
      torch.testing.assert_close(output_tensor, expected_tensor)
  
file_path: NeMo/tests/collections/tts/parts/preprocessing/test_feature_processors.py
imports: import contextlib
  import json
  import tempfile
  from pathlib import Path
  import numpy
  import pytest
  import torch
  from nemo.collections.tts.parts.preprocessing.feature_processors import FeatureScaler, LogCompression, MeanVarianceNormalization, MeanVarianceSpeakerNormalization
label_desc: Use image node_id 5 for calling the TestTTSFeatureProcessors obj's underlying member method code with example usage: obj.test_mean_variance_normalization() without return types