code: def GatedLinear(in_features, out_features, dropout=0.0, bias=True):
      """Weight-normalized Linear layer (input: B x T x C) with interspersed GLU units"""
      return nn.Sequential(
          Linear(in_features, out_features * 4, dropout, bias),
          nn.GLU(),
          Linear(out_features * 2, out_features * 2, dropout, bias),
          nn.GLU(),
          Linear(out_features, out_features, dropout, bias),
      )
  
file_path: fairseq/fairseq/modules/downsampled_multihead_attention.py
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from fairseq.modules.scalar_bias import scalar_bias
label_desc: Use image node_id 2 for calling a global function with example usage: GatedLinear(in_features, out_features, dropout, bias) and returns: nn