code: def test_log_compression(self):
      field = "test_feat"
  
      input_tensor = torch.tensor([-0.5, 0.0, 2.0], dtype=torch.float32)
      expected_tensor = torch.tensor(
          [np.log(0.5), 0.0, np.log(3.0)], dtype=torch.float32
      )
      processor = LogCompression(field)
  
      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 3 for calling the TestTTSFeatureProcessors obj's underlying member method code with example usage: obj.test_log_compression() without return types