code: def initialize_non_glu(module, input_dim, output_dim):
      gain_value = np.sqrt(
          (input_dim + output_dim) / np.sqrt(4 * input_dim)
      )
      torch.nn.init.xavier_normal_(module.weight, gain=gain_value)
      # torch.nn.init.zeros_(module.bias)
      return
  
imports: import torch
  from torch.nn import Linear, BatchNorm1d, ReLU
  import numpy
  from pytorch_tabnet import sparsemax
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_non_glu(module, input_dim, output_dim) without return types