code: def __init__(
      self,
      in_channels,
      out_channels,
      kernel_size,
      stride,
      padding,
      groups=1,
      **kwargs,
  ):
      super().__init__()
      weight_attr = paddle.ParamAttr(
          learning_rate=1, initializer=nn.initializer.KaimingUniform()
      )
      self._conv = nn.Conv2D(
          in_channels,
          out_channels,
          kernel_size,
          padding=padding,
          stride=stride,
          groups=groups,
          weight_attr=weight_attr,
          bias_attr=False,
          **kwargs,
      )
  
      self._batch_norm = layers.SyncBatchNorm(out_channels)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager, param_init
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new _ConvBN object from inherited base classes: nn with example: obj = _ConvBN(in_channels, out_channels, kernel_size, stride, padding, groups)