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__(self, in_features, out_feature):
      super(Highway, self).__init__()
      self.H = nn.Linear(in_features, out_feature)
      self.H.bias.data.zero_()
      self.T = nn.Linear(in_features, out_feature)
      self.T.bias.data.fill_(-1)
      self.relu = nn.ReLU()
      self.sigmoid = nn.Sigmoid()
  
imports: import torch
  from torch import nn
  from .common_layers import Prenet
  from .attentions import init_attn
label_desc: Use image node_id 1 to create a new Highway object from inherited base classes: nn with example: obj = Highway(in_features, out_feature)