class_docstr: Highway layers as explained in https://arxiv.org/abs/1505.00387
  
  Args:
      in_features (int): size of each input sample
      out_feature (int): size of each output sample
  
  Shapes:
      - input: (B, *, H_in)
      - output: (B, *, H_out)
class_docstr_tok: ["Highway","layers","as","explained","in","https",":","\/\/arxiv.org\/abs\/1505.00387","Args",":","in_features","(","int",")",":","size","of","each","input","sample","out_feature","(","int",")",":","size","of","each","output","sample","Shapes",":","-","input",":","(","B",",","*",",","H_in",")","-","output",":","(","B",",","*",",","H_out",")"]
code: def init_layers(self):
      torch.nn.init.xavier_uniform_(
          self.H.weight, gain=torch.nn.init.calculate_gain("relu")
      )
      torch.nn.init.xavier_uniform_(
          self.T.weight, gain=torch.nn.init.calculate_gain("sigmoid")
      )
  
imports: import torch
  from torch import nn
  from .common_layers import Prenet
  from .attentions import init_attn
label_desc: Use image node_id 2 for calling the Highway obj's underlying member method code with example usage: obj.init_layers() without return types