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 forward(self, inputs):
      H = self.relu(self.H(inputs))
      T = self.sigmoid(self.T(inputs))
      return H * T + inputs * (1.0 - T)
  
imports: import torch
  from torch import nn
  from .common_layers import Prenet
  from .attentions import init_attn
label_desc: Use image node_id 3 for calling the Highway obj's underlying member method code with example usage: obj.forward(inputs) and returns: unknown