class_docstr: Basic conv bn relu layer.
class_docstr_tok: ["Basic","conv","bn","relu","layer","."]
code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: int,
      stride: int = 1,
      dilation: int = 1,
      groups: int = 1,
      is_vd_mode: bool = False,
      act: str = None,
      name: str = None,
  ):
      super(ConvBNLayer, self).__init__()
  
      self.is_vd_mode = is_vd_mode
      self._pool2d_avg = AvgPool2D(
          kernel_size=2, stride=2, padding=0, ceil_mode=True
      )
      self._conv = Conv2D(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=(kernel_size - 1) // 2 if dilation == 1 else 0,
          dilation=dilation,
          groups=groups,
          bias_attr=False,
      )
  
      self._batch_norm = SyncBatchNorm(out_channels)
      self._act_op = Activation(act=act)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn import AvgPool2D
  from paddle.nn import Conv2D
  from paddle.nn.layer import activation
label_desc: Use image node_id 1 to create a new ConvBNLayer object from inherited base classes: nn with example: obj = ConvBNLayer(in_channels, out_channels, kernel_size, stride, dilation, groups, is_vd_mode, act, name)