code: def compute_madd(module, inp, out):
      if isinstance(module, nn.Conv2d):
          return compute_Conv2d_madd(module, inp[0], out[0])
      elif isinstance(module, nn.ConvTranspose2d):
          return compute_ConvTranspose2d_madd(module, inp[0], out[0])
      elif isinstance(module, nn.BatchNorm2d):
          return compute_BatchNorm2d_madd(module, inp[0], out[0])
      elif isinstance(module, nn.MaxPool2d):
          return compute_MaxPool2d_madd(module, inp[0], out[0])
      elif isinstance(module, nn.AvgPool2d):
          return compute_AvgPool2d_madd(module, inp[0], out[0])
      elif isinstance(module, (nn.ReLU, nn.ReLU6)):
          return compute_ReLU_madd(module, inp[0], out[0])
      elif isinstance(module, nn.Softmax):
          return compute_Softmax_madd(module, inp[0], out[0])
      elif isinstance(module, nn.Linear):
          return compute_Linear_madd(module, inp[0], out[0])
      elif isinstance(module, nn.Bilinear):
          return compute_Bilinear_madd(module, inp[0], inp[1], out)
      else:
          # print(f"[MAdd]: {type(module).__name__} is not supported!")
          return 0
  
file_path: tensorwatch/tensorwatch/model_graph/torchstat/compute_madd.py
imports: import torch.nn
label_desc: Use image node_id 10 for calling a global function with example usage: compute_madd(module, inp, out) and returns: compute_Conv2d_madd, compute_ConvTranspose2d_madd, compute_BatchNorm2d_madd, compute_MaxPool2d_madd, compute_AvgPool2d_madd, compute_ReLU_madd, compute_Softmax_madd, compute_Linear_madd, compute_Bilinear_madd, int