class_docstr: Sequential RNNLM.
  
  See also:
      https://github.com/pytorch/examples/blob/4581968193699de14b56527296262dd76ab43557/word_language_model/model.py
class_docstr_tok: ["Sequential","RNNLM",".","See","also",":","https",":","\/\/github.com\/pytorch\/examples\/blob\/4581968193699de14b56527296262dd76ab43557\/word_language_model\/model.py"]
docstr: Get an initial state for decoding.
  
  Args:
      x (torch.Tensor): The encoded feature tensor
  
  Returns: initial state
docstr_tok: ["Get","an","initial","state","for","decoding",".","Args",":","x","(","torch.Tensor",")",":","The","encoded","feature","tensor","Returns",":","initial","state"]
code: def init_state(self, x):
      """Get an initial state for decoding.
  
      Args:
          x (torch.Tensor): The encoded feature tensor
  
      Returns: initial state
  
      """
      bsz = 1
      weight = next(self.parameters())
      if self.rnn_type == "LSTM":
          return (
              weight.new_zeros(self.nlayers, bsz, self.nhid),
              weight.new_zeros(self.nlayers, bsz, self.nhid),
          )
      else:
          return weight.new_zeros(self.nlayers, bsz, self.nhid)
  
file_path: espnet/espnet/nets/pytorch_backend/lm/seq_rnn.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from espnet.nets.lm_interface import LMInterface
label_desc: Use image node_id 7 for calling the SequentialRNNLM obj's underlying member method code with example usage: obj.init_state(x) and returns: weight,  weight, weight