code: def __init__(
      self,
      a,
      b,
      ks=1,
      stride=1,
      pad=0,
      dilation=1,
      groups=1,
      bn_weight_init=1.0,
  ):
      super().__init__()
      self.add_sublayer(
          "c",
          nn.Conv2D(
              a, b, ks, stride, pad, dilation, groups, bias_attr=False
          ),
      )
      bn = nn.BatchNorm2D(b)
      self.add_sublayer("bn", bn)
  
imports: import itertools
  import paddle
  from paddle import nn
  from paddle.nn import functional
  from .common import LayerNorm2d
label_desc: Use image node_id 1 to create a new Conv2d_BN object from inherited base classes: nn with example: obj = Conv2d_BN(a, b, ks, stride, pad, dilation, groups, bn_weight_init)