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_parameters()
docstr_tok: ["reset_parameters","(",")"]
code: def reset_parameters(self, gain=1):
      """
      reset_parameters()
      """
      stdev = 1.0 / math.sqrt(self.hidden_size)
      for param in self.parameters():
          param.data.uniform_(-stdev, stdev)
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  import torch.nn.functional
  import math
label_desc: Use image node_id 3 for calling the RNNCell obj's underlying member method code with example usage: obj.reset_parameters(gain) without return types