code: def _rnn_cell_forward_hook(rnn_cell_module, input, output):
      flops = 0
      inp = input[0]
      batch_size = inp.shape[0]
      w_ih = rnn_cell_module.__getattr__("weight_ih")
      w_hh = rnn_cell_module.__getattr__("weight_hh")
      input_size = inp.shape[1]
      flops = _rnn_flops(flops, rnn_cell_module, w_ih, w_hh, input_size)
      if rnn_cell_module.bias:
          b_ih = rnn_cell_module.__getattr__("bias_ih")
          b_hh = rnn_cell_module.__getattr__("bias_hh")
          flops += b_ih.shape[0] + b_hh.shape[0]
  
      flops *= batch_size
      rnn_cell_module.__flops__ += int(flops)
  
imports: import time
  import torch
  import torch.nn
  import torch.nn.functional
  from functools import partial
  from typing import List, Optional
  from collections import OrderedDict
  import numpy
label_desc: Use image node_id 35 for calling a global function with example usage: _rnn_cell_forward_hook(rnn_cell_module, input, output) without return types