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: forward()
  if not inited or bsz has changed this will create hidden states
docstr_tok: ["forward","(",")","if","not","inited","or","bsz","has","changed","this","will","create","hidden","states"]
code: def forward(self, input):
      """
      forward()
      if not inited or bsz has changed this will create hidden states
      """
      self.init_hidden(input.size()[0])
  
      hidden_state = (
          self.hidden[0] if self.n_hidden_states == 1 else self.hidden
      )
      self.hidden = self.cell(
          input,
          hidden_state,
          self.w_ih,
          self.w_hh,
          b_ih=self.b_ih,
          b_hh=self.b_hh,
      )
      if self.n_hidden_states > 1:
          self.hidden = list(self.hidden)
      else:
          self.hidden = [self.hidden]
  
      if self.output_size != self.hidden_size:
          self.hidden[0] = F.linear(self.hidden[0], self.w_ho)
  
      return tuple(self.hidden)
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  import torch.nn.functional
  import math
label_desc: Use image node_id 7 for calling the RNNCell obj's underlying member method code with example usage: obj.forward(input) and returns: tuple