code: def test_save_and_load_energy(self):
      energy_name = "energy_test"
      mel_featurizer = MelSpectrogramFeaturizer(
          mel_dim=self.spec_dim,
          hop_length=self.hop_len,
          sample_rate=self.sample_rate,
      )
      energy_featurizer = EnergyFeaturizer(
          feature_name=energy_name, spec_featurizer=mel_featurizer
      )
  
      with self._create_test_dir() as test_dir:
          feature_dir = test_dir / "feature"
          energy_featurizer.save(
              manifest_entry=self.manifest_entry,
              audio_dir=test_dir,
              feature_dir=feature_dir,
          )
          energy_dict = energy_featurizer.load(
              manifest_entry=self.manifest_entry,
              audio_dir=test_dir,
              feature_dir=feature_dir,
          )
  
      energy = energy_dict[energy_name]
      assert len(energy.shape) == 1
      assert energy.shape[0] == self.spec_len
      assert energy.dtype == torch.float32
  
imports: import contextlib
  import tempfile
  from pathlib import Path
  import numpy
  import pytest
  import soundfile
  import torch
  from nemo.collections.tts.parts.preprocessing.features import EnergyFeaturizer, MelSpectrogramFeaturizer, PitchFeaturizer
label_desc: Use image node_id 11 for calling the TestTTSFeatures obj's underlying member method code with example usage: obj.test_save_and_load_energy() without return types