code: def __init__(
      self,
      in_c,
      out_c,
      filter_size,
      stride,
      padding,
      num_groups=1,
      act=None,
      lr_mult=1.0,
      conv_decay=0.0,
      norm_type="bn",
      norm_decay=0.0,
      freeze_norm=False,
      name="",
  ):
      super(ConvBNLayer, self).__init__()
      self.act = act
      self.conv = nn.Conv2D(
          in_channels=in_c,
          out_channels=out_c,
          kernel_size=filter_size,
          stride=stride,
          padding=padding,
          groups=num_groups,
          weight_attr=ParamAttr(
              learning_rate=lr_mult, regularizer=L2Decay(conv_decay)
          ),
          bias_attr=False,
      )
  
      norm_lr = 0.0 if freeze_norm else lr_mult
      param_attr = ParamAttr(
          learning_rate=norm_lr,
          regularizer=L2Decay(norm_decay),
          trainable=False if freeze_norm else True,
      )
      bias_attr = ParamAttr(
          learning_rate=norm_lr,
          regularizer=L2Decay(norm_decay),
          trainable=False if freeze_norm else True,
      )
      global_stats = True if freeze_norm else None
      if norm_type in ["sync_bn", "bn"]:
          self.bn = nn.BatchNorm2D(
              out_c,
              weight_attr=param_attr,
              bias_attr=bias_attr,
              use_global_stats=global_stats,
          )
      norm_params = self.bn.parameters()
      if freeze_norm:
          for param in norm_params:
              param.stop_gradient = True
  
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 ppdet.core.workspace import register, serializable
  from numbers import Integral
  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(in_c, out_c, filter_size, stride, padding, num_groups, act, lr_mult, conv_decay, norm_type, norm_decay, freeze_norm, name)