class_docstr: Independent GLU block, specific to each step
class_docstr_tok: ["Independent","GLU","block",",","specific","to","each","step"]
code: def forward(self, x):
      scale = torch.sqrt(torch.FloatTensor([0.5]).to(x.device))
      if (
          self.first
      ):  # the first layer of the block has no scale multiplication
          x = self.glu_layers[0](x)
          layers_left = range(1, self.n_glu)
      else:
          layers_left = range(self.n_glu)
  
      for glu_id in layers_left:
          x = torch.add(x, self.glu_layers[glu_id](x))
          x = x * scale
      return x
  
imports: import torch
  from torch.nn import Linear, BatchNorm1d, ReLU
  import numpy
  from pytorch_tabnet import sparsemax
label_desc: Use image node_id 2 for calling the GLU_Block obj's underlying member method code with example usage: obj.forward(x) and returns: x