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:]
      flops = (
          batch_size
          * reduce(operator.mul, w_shape)
          * reduce(operator.mul, conv_shape)
      )
      return flops
  
imports: import operator
  from collections import defaultdict
  from contextlib import contextmanager
  from enum import Enum, auto
  from functools import partial, reduce
  from numbers import Number
  from typing import Any, Callable, List, Union
  import torch
  from packaging import version
  from torch.utils._pytree import tree_map
  from .meta_tensor import MetaTensor
label_desc: Use image node_id 8 for calling a global function with example usage: conv_flop_count(x_shape, w_shape, out_shape, transposed) and returns: flops