code: def __init__(self, in_channels, mid_channels, dilation):
      super(Bottleneck, self).__init__()
      self.conv1 = nn.Sequential(
          *[
              nn.Conv2D(
                  in_channels,
                  mid_channels,
                  1,
                  padding=0,
                  weight_attr=ParamAttr(
                      initializer=Normal(mean=0, std=0.01)
                  ),
                  bias_attr=ParamAttr(initializer=Constant(0.0)),
              ),
              nn.BatchNorm2D(
                  mid_channels,
                  weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
                  bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
              ),
              nn.ReLU(),
          ]
      )
      self.conv2 = nn.Sequential(
          *[
              nn.Conv2D(
                  mid_channels,
                  mid_channels,
                  3,
                  padding=dilation,
                  dilation=dilation,
                  weight_attr=ParamAttr(
                      initializer=Normal(mean=0, std=0.01)
                  ),
                  bias_attr=ParamAttr(initializer=Constant(0.0)),
              ),
              nn.BatchNorm2D(
                  mid_channels,
                  weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
                  bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
              ),
              nn.ReLU(),
          ]
      )
      self.conv3 = nn.Sequential(
          *[
              nn.Conv2D(
                  mid_channels,
                  in_channels,
                  1,
                  padding=0,
                  weight_attr=ParamAttr(
                      initializer=Normal(mean=0, std=0.01)
                  ),
                  bias_attr=ParamAttr(initializer=Constant(0.0)),
              ),
              nn.BatchNorm2D(
                  in_channels,
                  weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
                  bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
              ),
              nn.ReLU(),
          ]
      )
  
file_path: PaddleDetection/ppdet/modeling/necks/dilated_encoder.py
imports: import paddle
  import paddle.nn
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
  from paddle.nn.initializer import KaimingUniform, Constant, Normal
  from ppdet.core.workspace import register, serializable
  from ..shape_spec import ShapeSpec
label_desc: Use image node_id 1 to create a new Bottleneck object from inherited base classes: nn with example: obj = Bottleneck(in_channels, mid_channels, dilation)