code: def test_feature_scalar(self):
      field = "test_feat"
      input_tensor = torch.tensor([-2.5, 0.0, 1.0], dtype=torch.float32)
      expected_tensor = torch.tensor(
          [0.0, 2.0, 2.8], dtype=torch.float32
      )
      processor = FeatureScaler(field, add_value=2.5, div_value=1.25)
  
      training_example = {field: input_tensor}
      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 2 for calling the TestTTSFeatureProcessors obj's underlying member method code with example usage: obj.test_feature_scalar() without return types