code: def _rnn_forward_hook(rnn_module, input, output):
      flops = 0
      # input is a tuple containing a sequence to process and (optionally) hidden state
      inp = input[0]
      batch_size = inp.shape[0]
      seq_length = inp.shape[1]
      num_layers = rnn_module.num_layers
  
      for i in range(num_layers):
          w_ih = rnn_module.__getattr__("weight_ih_l" + str(i))
          w_hh = rnn_module.__getattr__("weight_hh_l" + str(i))
          if i == 0:
              input_size = rnn_module.input_size
          else:
              input_size = rnn_module.hidden_size
          flops = _rnn_flops(flops, rnn_module, w_ih, w_hh, input_size)
          if rnn_module.bias:
              b_ih = rnn_module.__getattr__("bias_ih_l" + str(i))
              b_hh = rnn_module.__getattr__("bias_hh_l" + str(i))
              flops += b_ih.shape[0] + b_hh.shape[0]
  
      flops *= batch_size
      flops *= seq_length
      if rnn_module.bidirectional:
          flops *= 2
      rnn_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 34 for calling a global function with example usage: _rnn_forward_hook(rnn_module, input, output) without return types