class_docstr: Normalize the spectrogram with a minimum db value
code: def forward(self, specgram):
      specgram = torch.log10(torch.clamp(specgram.squeeze(0), min=1e-5))
      if self.normalization:
          return torch.clamp(
              (self.min_level_db - 20 * specgram) / self.min_level_db,
              min=0,
              max=1,
          )
      return specgram
  
file_path: audio/examples/pipeline_wavernn/processing.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the NormalizeDB obj's underlying member method code with example usage: obj.forward(specgram) and returns: specgram, torch