code: def conv_backward_flop(inputs: List[Any], outputs: List[Any]):
      grad_out_shape, x_shape, w_shape = [
          get_shape(i) for i in inputs[:3]
      ]
      output_mask = inputs[-1]
      fwd_transposed = inputs[7]
      flop_count = 0
  
      if output_mask[0]:
          grad_input_shape = get_shape(outputs[0])
          flop_count += conv_flop_count(
              grad_out_shape,
              w_shape,
              grad_input_shape,
              not fwd_transposed,
          )
      if output_mask[1]:
          grad_weight_shape = get_shape(outputs[1])
          flop_count += conv_flop_count(
              transpose_shape(x_shape),
              grad_out_shape,
              grad_weight_shape,
              fwd_transposed,
          )
  
      return flop_count
  
imports: import os
  from collections import defaultdict
  from numbers import Number
  from typing import Any, List
  import torch
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import tree_map
  from torchvision.models._api import Weights
label_desc: Use image node_id 10 for calling a global function with example usage: conv_backward_flop(inputs, outputs) and returns: flop_count