class_docstr: Global Layer Normalization (gLN).
  
  Arguments
  ---------
  channel_size : int
      Number of channels in the third dimension.
  
  Example
  -------
  >>> x = torch.randn(2, 3, 3)
  >>> norm_func = GlobalLayerNorm(3)
  >>> x_normalized = norm_func(x)
  >>> x.shape
  torch.Size([2, 3, 3])
code: def __init__(self, channel_size):
      super(GlobalLayerNorm, self).__init__()
      self.gamma = nn.Parameter(
          torch.Tensor(1, 1, channel_size)
      )  # [1, 1, N]
      self.beta = nn.Parameter(
          torch.Tensor(1, 1, channel_size)
      )  # [1, 1, N]
      self.reset_parameters()
  
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 1 to create a new GlobalLayerNorm object from inherited base classes: nn with example: obj = GlobalLayerNorm(channel_size)