class_docstr: A `Highway layer <https://arxiv.org/abs/1505.00387>`_.
  Adopted from the AllenNLP implementation.
class_docstr_tok: ["A","`","Highway","layer","<","https",":","\/\/arxiv.org\/abs\/1505.00387",">","`","_",".","Adopted","from","the","AllenNLP","implementation","."]
code: def reset_parameters(self):
      for layer in self.layers:
          # As per comment in AllenNLP:
          # We should bias the highway layer to just carry its input forward.  We do that by
          # setting the bias on `B(x)` to be positive, because that means `g` will be biased to
          # be high, so we will carry the input forward.  The bias on `B(x)` is the second half
          # of the bias vector in each Linear layer.
          nn.init.constant_(layer.bias[self.input_dim :], 1)
  
          nn.init.constant_(layer.bias[: self.input_dim], 0)
          nn.init.xavier_normal_(layer.weight)
  
file_path: fairseq/fairseq/modules/character_token_embedder.py
imports: import logging
  from typing import List, Tuple
  import torch
  import torch.nn.functional
  from fairseq.data import Dictionary
  from torch import nn
label_desc: Use image node_id 2 for calling the Highway obj's underlying member method code with example usage: obj.reset_parameters() without return types