code: def __init__(
      self,
      a,
      b,
      ks=1,
      stride=1,
      pad=0,
      dilation=1,
      groups=1,
      bn_weight_init=1,
      resolution=-10000,
  ):
      super().__init__()
      self.add_sublayer(
          "c",
          nn.Conv2D(
              a, b, ks, stride, pad, dilation, groups, bias_attr=False
          ),
      )
      bn = nn.BatchNorm2D(b)
      ones_(bn.weight)
      zeros_(bn.bias)
      self.add_sublayer("bn", bn)
  
imports: import itertools
  import math
  import warnings
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import Constant
  from paddle.nn.initializer import TruncatedNormal
  from paddle.regularizer import L2Decay
  from .vision_transformer import Identity
  from .vision_transformer import ones_
  from .vision_transformer import trunc_normal_
  from .vision_transformer import zeros_
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, resolution)