code: def _conv_flop_count(
      x_shape: List[int],
      w_shape: List[int],
      out_shape: List[int],
      transposed: bool = False,
  ) -> Number:
      """
      Count flops for convolution. Note only multiplication is
      counted. Computation for addition and bias is ignored.
      Flops for a transposed convolution are calculated as
      flops = (x_shape[2:] * prod(w_shape) * batch_size).
      Args:
          x_shape (list(int)): The input shape before convolution.
          w_shape (list(int)): The filter shape.
          out_shape (list(int)): The output shape after convolution.
          transposed (bool): is the convolution transposed
      Returns:
          int: the number of flops
      """
      batch_size = x_shape[0]
      conv_shape = (x_shape if transposed else out_shape)[2:]
      flop = (
          batch_size
          * reduce(operator.mul, w_shape, 1)
          * reduce(operator.mul, conv_shape, 1)
      )
      return flop
  
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 4 for calling a global function with example usage: _conv_flop_count(x_shape, w_shape, out_shape, transposed) and returns: flop