code: def __init__(
      self,
      in_channels,
      out_channels,
      num_blocks=3,
      expansion=1.0,
      bias=False,
      act="silu",
  ):
      super(CSPRepLayer, self).__init__()
      hidden_channels = int(out_channels * expansion)
      self.conv1 = BaseConv(
          in_channels,
          hidden_channels,
          ksize=1,
          stride=1,
          bias=bias,
          act=act,
      )
      self.conv2 = BaseConv(
          in_channels,
          hidden_channels,
          ksize=1,
          stride=1,
          bias=bias,
          act=act,
      )
      self.bottlenecks = nn.Sequential(
          *[
              RepVggBlock(hidden_channels, hidden_channels, act=act)
              for _ in range(num_blocks)
          ]
      )
      if hidden_channels != out_channels:
          self.conv3 = BaseConv(
              hidden_channels,
              out_channels,
              ksize=1,
              stride=1,
              bias=bias,
              act=act,
          )
      else:
          self.conv3 = nn.Identity()
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register, serializable
  from ppdet.modeling.ops import get_act_fn
  from ..shape_spec import ShapeSpec
  from ..backbones.csp_darknet import BaseConv
  from ..backbones.cspresnet import RepVggBlock
  from ppdet.modeling.transformers.detr_transformer import TransformerEncoder
  from ..initializer import xavier_uniform_, linear_init_
  from ..layers import MultiHeadAttention
  from paddle import ParamAttr
  from paddle.regularizer import L2Decay
label_desc: Use image node_id 1 to create a new CSPRepLayer object from inherited base classes: nn with example: obj = CSPRepLayer(in_channels, out_channels, num_blocks, expansion, bias, act)