content: import pytorch_lightning as pl
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from pointnet2_ops.pointnet2_modules import PointnetSAModule, PointnetSAModuleMSG
  
  from pointnet2.models.pointnet2_ssg_cls import PointNet2ClassificationSSG
  
  
  class PointNet2ClassificationMSG(PointNet2ClassificationSSG):
      def _build_model(self):
          super()._build_model()
  
          self.SA_modules = nn.ModuleList()
          self.SA_modules.append(
              PointnetSAModuleMSG(
                  npoint=512,
                  radii=[0.1, 0.2, 0.4],
                  nsamples=[16, 32, 128],
                  mlps=[[3, 32, 32, 64], [3, 64, 64, 128], [3, 64, 96, 128]],
                  use_xyz=self.hparams["model.use_xyz"],
              )
          )
  
          input_channels = 64 + 128 + 128
          self.SA_modules.append(
              PointnetSAModuleMSG(
                  npoint=128,
                  radii=[0.2, 0.4, 0.8],
                  nsamples=[32, 64, 128],
                  mlps=[
                      [input_channels, 64, 64, 128],
                      [input_channels, 128, 128, 256],
                      [input_channels, 128, 128, 256],
                  ],
                  use_xyz=self.hparams["model.use_xyz"],
              )
          )
          self.SA_modules.append(
              PointnetSAModule(
                  mlp=[128 + 256 + 256, 256, 512, 1024],
                  use_xyz=self.hparams["model.use_xyz"],
              )
          )
  