class_docstr: MultiBand sample processor. The input audio is splitted across
  frequency bands evenly distributed in mel-scale.
  
  Each band will be rescaled to match the power distribution
  of Gaussian noise in that band, using online metrics
  computed on the first few samples.
  
  Args:
      n_bands (int): Number of mel-bands to split the signal over.
      sample_rate (int): Sample rate of the audio.
      num_samples (int): Number of samples to use to fit the rescaling
          for each band. The processor won't be stable
          until it has seen that many samples.
      power_std (float or list/tensor): The rescaling factor computed to match the
          power of Gaussian noise in each band is taken to
          that power, i.e. `1.` means full correction of the energy
          in each band, and values less than `1` means only partial
          correction. Can be used to balance the relative importance
          of low vs. high freq in typical audio signals.
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code: def mean(self):
      mean = self.sum_x / self.counts
      return mean
  
imports: from collections import namedtuple
  import random
  import typing
  import julius
  import torch
label_desc: Use image node_id 2 for calling the MultiBandProcessor obj's underlying member method code with example usage: obj.mean() and returns: mean