class_docstr: Container module with an encoder, a recurrent module, and a decoder.
code: def init_hidden(self, bsz):
      weight = next(self.parameters()).data
      if self.rnn_type == "LSTM":
          return [
              (
                  weight.new(
                      1,
                      bsz,
                      self.nhid
                      if l != self.nlayers - 1
                      else (
                          self.ninp if self.tie_weights else self.nhid
                      ),
                  ).zero_(),
                  weight.new(
                      1,
                      bsz,
                      self.nhid
                      if l != self.nlayers - 1
                      else (
                          self.ninp if self.tie_weights else self.nhid
                      ),
                  ).zero_(),
              )
              for l in range(self.nlayers)
          ]
      elif self.rnn_type == "QRNN" or self.rnn_type == "GRU":
          return [
              weight.new(
                  1,
                  bsz,
                  self.nhid
                  if l != self.nlayers - 1
                  else (self.ninp if self.tie_weights else self.nhid),
              ).zero_()
              for l in range(self.nlayers)
          ]
  
file_path: awd-lstm-lm/model.py
imports: import torch
  import torch.nn
  from embed_regularize import embedded_dropout
  from locked_dropout import LockedDropout
  from weight_drop import WeightDrop
label_desc: Use image node_id 5 for calling the RNNModel obj's underlying member method code with example usage: obj.init_hidden(bsz) and returns: unknown, unknown