code: def complexity(self, cx):
      cx = conv2d_cx(
          cx,
          in_c=self.conv.in_channels,
          out_c=self.conv.out_channels,
          k=self.conv.kernel_size[0],  # tuple type
          stride=self.conv.stride[0],  # tuple type
          groups=self.conv.groups,
          bias=False,
          trainable=self.conv.weight.requires_grad,
      )
      cx = norm2d_cx(
          cx,
          self.conv.out_channels,
          trainable=self.bn.weight.requires_grad,
      )
  
      return cx
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Callable, Optional
  import torch.nn
  import torch
  from torch import Tensor
  from utils import *
label_desc: Use image node_id 3 for calling the ConvBNAct obj's underlying member method code with example usage: obj.complexity(cx) and returns: cx