class_docstr: Channel-wise Layer Normalization (cLN).
  
  Arguments
  ---------
  channel_size : int
      Number of channels in the normalization dimension (the third dimension).
  
  Example
  -------
  >>> x = torch.randn(2, 3, 3)
  >>> norm_func = ChannelwiseLayerNorm(3)
  >>> x_normalized = norm_func(x)
  >>> x.shape
  torch.Size([2, 3, 3])
docstr: Resets the parameters.
docstr_tok: ["Resets","the","parameters","."]
code: def reset_parameters(self):
      """Resets the parameters."""
      self.gamma.data.fill_(1)
      self.beta.data.zero_()
  
imports: import torch
  import torch.nn
  import speechbrain
  import torch.nn.functional
  from speechbrain.processing.signal_processing import overlap_and_add
label_desc: Use image node_id 2 for calling the ChannelwiseLayerNorm obj's underlying member method code with example usage: obj.reset_parameters() without return types