class_docstr: A Highway LSTM Cell as proposed in Zhang et al. (2018) Highway Long Short-Term Memory RNNs for 
  Distant Speech Recognition.
class_docstr_tok: ["A","Highway","LSTM","Cell","as","proposed","in","Zhang","et","al",".","(","2018",")","Highway","Long","Short-Term","Memory","RNNs","for","Distant","Speech","Recognition","."]
code: def __init__(self, input_size, hidden_size, bias=True):
      super(HLSTMCell, self).__init__()
      self.input_size = input_size
      self.hidden_size = hidden_size
  
      # LSTM parameters
      self.Wi = nn.Linear(
          input_size + hidden_size, hidden_size, bias=bias
      )
      self.Wf = nn.Linear(
          input_size + hidden_size, hidden_size, bias=bias
      )
      self.Wo = nn.Linear(
          input_size + hidden_size, hidden_size, bias=bias
      )
      self.Wg = nn.Linear(
          input_size + hidden_size, hidden_size, bias=bias
      )
  
      # highway gate parameters
      self.gate = nn.Linear(
          input_size + 2 * hidden_size, hidden_size, bias=bias
      )
  
file_path: stanza/stanza/models/common/hlstm.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.utils.rnn import pad_packed_sequence, pack_padded_sequence, pack_sequence, PackedSequence
  from stanza.models.common.packed_lstm import PackedLSTM
label_desc: Use image node_id 1 to create a new HLSTMCell object from inherited base classes: nn.modules.rnn with example: obj = HLSTMCell(input_size, hidden_size, bias)