code: def __init__(
      self,
      ch_in,
      ch_out,
      filter_size=3,
      stride=1,
      groups=1,
      padding=0,
      norm_cfg={"name": "gn", "num_groups": 32},
      act=None,
  ):
      super(ConvBNLayer, self).__init__()
  
      self.conv = nn.Conv2D(
          in_channels=ch_in,
          out_channels=ch_out,
          kernel_size=filter_size,
          stride=stride,
          padding=padding,
          groups=groups,
          bias_attr=False,
      )
  
      norm_type = norm_cfg["name"]
      if norm_type in ["sync_bn", "bn"]:
          self.norm = nn.BatchNorm2D(
              ch_out,
              weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
              bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
          )
      else:
          groups = norm_cfg.get("num_groups", 1)
          self.norm = nn.GroupNorm(
              num_groups=groups,
              num_channels=ch_out,
              weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
              bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
          )
      self.act = (
          get_act_fn(act)
          if act is None or isinstance(act, (str, dict))
          else act
      )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from .fcos_head import ScaleReg
  from ..initializer import bias_init_with_prob, constant_, normal_
  from ..ops import get_act_fn, anchor_generator
  from ..rbox_utils import box2corners
  from ..losses import ProbIoULoss
  import numpy
label_desc: Use image node_id 1 to create a new ConvBNLayer object from inherited base classes: nn with example: obj = ConvBNLayer(ch_in, ch_out, filter_size, stride, groups, padding, norm_cfg, act)