code: def __init__(
      self,
      ch_in,
      ch_out,
      filter_size=3,
      stride=1,
      groups=1,
      padding=0,
      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,
      )
  
      self.bn = nn.BatchNorm2D(
          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: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import Constant
  from ppdet.modeling.ops import get_act_fn
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
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, act)