class_docstr: RNNCell 
  gate_multiplier is related to the architecture you're working with
  For LSTM-like it will be 4 and GRU-like will be 3.
  Always assumes input is NOT batch_first.
  Output size that's not hidden size will use output projection
  Hidden_states is number of hidden states that are needed for cell
  if one will go directly to cell as tensor, if more will go as list
class_docstr_tok: ["RNNCell","gate_multiplier","is","related","to","the","architecture","you","'re","working","with","For","LSTM-like","it","will","be","4","and","GRU-like","will","be","3",".","Always","assumes","input","is","NOT","batch_first",".","Output","size","that","'s","not","hidden","size","will","use","output","projection","Hidden_states","is","number","of","hidden","states","that","are","needed","for","cell","if","one","will","go","directly","to","cell","as","tensor",",","if","more","will","go","as","list"]
docstr: reset_hidden()
docstr_tok: ["reset_hidden","(",")"]
code: def reset_hidden(self, bsz):
      """
      reset_hidden()
      """
      for i, _ in enumerate(self.hidden):
          self.hidden[i] = None
      self.init_hidden(bsz)
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  import torch.nn.functional
  import math
label_desc: Use image node_id 5 for calling the RNNCell obj's underlying member method code with example usage: obj.reset_hidden(bsz) without return types