code: def _conv_flop_jit(
      inputs: Tuple[
          Any
      ],  # pyre-ignore [2] the inputs can be union of Tensor/bool/Tuple
      outputs: Tuple[torch.Tensor],
  ) -> Number:
      """
      Count flops for convolution.
      """
      x: torch.Tensor = inputs[0]
      w: torch.Tensor = inputs[1]
      x_shape, w_shape, out_shape = (x.shape, w.shape, outputs[0].shape)
      transposed: bool = inputs[6]
  
      return _conv_flop_count(
          list(x_shape),
          list(w_shape),
          list(out_shape),
          transposed=transposed,
      )
  
imports: import logging
  import operator
  from collections import defaultdict
  from functools import reduce
  from numbers import Number
  from typing import Any, Callable, DefaultDict, Dict, List, Tuple
  import torch
  from torch.utils._python_dispatch import TorchDispatchMode
  from torch.utils._pytree import PyTree, tree_map
label_desc: Use image node_id 5 for calling a global function with example usage: _conv_flop_jit(inputs, outputs) and returns: _conv_flop_count