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: new_like()
docstr_tok: ["new_like","(",")"]
code: def new_like(self, new_input_size=None):
      """
      new_like()
      """
      if new_input_size is None:
          new_input_size = self.input_size
  
      return type(self)(
          self.gate_multiplier,
          new_input_size,
          self.hidden_size,
          self.cell,
          self.n_hidden_states,
          self.bias,
          self.output_size,
      )
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  import torch.nn.functional
  import math
label_desc: Use image node_id 2 for calling the RNNCell obj's underlying member method code with example usage: obj.new_like(new_input_size) and returns: type