code: def mean_std_norm(waveforms, dims=1, eps=1e-06):
      """This function normalizes the mean and std of the input
          waveform (along the specified axis).
  
      Arguments
      ---------
      waveforms : tensor
          The waveforms to normalize.
          Shape should be `[batch, time]` or `[batch, time, channels]`.
      dim: int or tuple
          The dimension(s) on which mean and std are computed
      eps : float
          A small number to add to the denominator to prevent NaN.
  
      Returns
      -------
      waveforms : tensor
          Normalized level waveform.
      """
      mean = waveforms.mean(dims, keepdim=True)
      std = waveforms.std(dims, keepdim=True)
      waveforms = (waveforms - mean) / (std + eps)
      return waveforms
  
file_path: speechbrain/speechbrain/processing/signal_processing.py
imports: import torch
  import math
  from packaging import version
label_desc: Use image node_id 3 for calling a global function with example usage: mean_std_norm(waveforms, dims, eps) and returns: waveforms