code: def conv_backward_flop(
      grad_out_shape,
      x_shape,
      w_shape,
      _bias,
      _stride,
      _padding,
      _dilation,
      transposed,
      _output_padding,
      _groups,
      output_mask,
      out_shape,
  ) -> int:
      flop_count = 0
  
      if output_mask[0]:
          grad_input_shape = get_shape(out_shape[0])
          flop_count += conv_flop_count(
              grad_out_shape, w_shape, grad_input_shape, not transposed
          )
      if output_mask[1]:
          grad_weight_shape = get_shape(out_shape[1])
          flop_count += conv_flop_count(
              transpose_shape(x_shape),
              grad_out_shape,
              grad_weight_shape,
              transposed,
          )
  
      return flop_count
  
imports: import torch
  import torch.nn
  from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
  from typing import List, Any, Dict, Optional, Union, NamedTuple
  from collections import defaultdict
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils.hooks import RemovableHandle
  from torch._decomp import register_decomposition
  from math import prod
  from functools import wraps
label_desc: Use image node_id 11 for calling a global function with example usage: conv_backward_flop(grad_out_shape, x_shape, w_shape, _bias, _stride, _padding, _dilation, transposed, _output_padding, _groups, output_mask, out_shape) and returns: flop_count