code: def matmul_flop_jit(inputs: List[Any], outputs: List[Any]) -> Number:
      """
      Count flops for matmul.
      """
      # Inputs should be a list of length 2.
      # Inputs contains the shapes of two matrices.
      input_shapes = [v.shape for v in inputs]
      assert len(input_shapes) == 2, input_shapes
  
      # There are three cases: 1) gemm, 2) gemv, 3) dot
      if all(len(shape) == 2 for shape in input_shapes):
          # gemm
          assert (
              input_shapes[0][-1] == input_shapes[1][-2]
          ), input_shapes
      elif all(len(shape) == 1 for shape in input_shapes):
          # dot
          assert input_shapes[0][0] == input_shapes[1][0], input_shapes
  
          # expand shape
          input_shapes[0] = torch.Size([1, input_shapes[0][0]])
          input_shapes[1] = torch.Size([input_shapes[1][0], 1])
      else:
          # gemv
          if len(input_shapes[0]) == 1:
              assert (
                  input_shapes[0][0] == input_shapes[1][-2]
              ), input_shapes
              input_shapes.reverse()
          else:
              assert (
                  input_shapes[1][0] == input_shapes[0][-1]
              ), input_shapes
  
          # expand the shape of the vector to [batch size, 1]
          input_shapes[-1] = torch.Size([input_shapes[-1][-1], 1])
      flops = (
          reduce(operator.mul, input_shapes[0]) * input_shapes[-1][-1]
      )
      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 4 for calling a global function with example usage: matmul_flop_jit(inputs, outputs) and returns: flops