class_docstr: Spatial pyramid pooling layer used in YOLOv3-SPP.
  
  Args:
      in_channels (int): The input channels of this Module.
      out_channels (int): The output channels of this Module.
      kernel_sizes (tuple[int]): Sequential of kernel sizes of pooling
          layers. Default: (5, 9, 13).
      conv_cfg (dict): Config dict for convolution layer. Default: None,
          which means using conv2d.
      norm_cfg (dict): Config dict for normalization layer.
          Default: dict(type='BN').
      act_cfg (dict): Config dict for activation layer.
          Default: dict(type='Swish').
      init_cfg (dict or list[dict], optional): Initialization config dict.
          Default: None.
code: def forward(self, x):
      x = self.conv1(x)
      with torch.cuda.amp.autocast(enabled=False):
          x = torch.cat(
              [x] + [pooling(x) for pooling in self.poolings], dim=1
          )
      x = self.conv2(x)
      return x
  
imports: import math
  import torch
  import torch.nn
  from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
  from mmengine.model import BaseModule
  from torch.nn.modules.batchnorm import _BatchNorm
  from mmdet.registry import MODELS
  from ..layers import CSPLayer
label_desc: Use image node_id 2 for calling the SPPBottleneck obj's underlying member method code with example usage: obj.forward(x) and returns: x