class_docstr: Featurizer that converts wavs to Mel Spectrograms.
  See AudioToMelSpectrogramPreprocessor for args.
class_docstr_tok: ["Featurizer","that","converts","wavs","to","Mel","Spectrograms",".","See","AudioToMelSpectrogramPreprocessor","for","args","."]
code: def log_zero_guard_value_fn(self, x):
      if isinstance(self.log_zero_guard_value, str):
          if self.log_zero_guard_value == "tiny":
              return torch.finfo(x.dtype).tiny
          elif self.log_zero_guard_value == "eps":
              return torch.finfo(x.dtype).eps
          else:
              raise ValueError(
                  f"{self} received {self.log_zero_guard_value} for the "
                  f"log_zero_guard_type parameter. It must be either a "
                  f"number, 'tiny', or 'eps'"
              )
      else:
          return self.log_zero_guard_value
  
imports: import math
  import random
  from typing import Optional, Tuple, Union
  import librosa
  import numpy
  import torch
  import torch.nn
  from nemo.collections.asr.parts.preprocessing.perturb import AudioAugmentor
  from nemo.collections.asr.parts.preprocessing.segment import AudioSegment
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the FilterbankFeatures obj's underlying member method code with example usage: obj.log_zero_guard_value_fn(x) and returns: self, torch, torch