text: import torch
  import torch.nn as nn
  from pointnet import PointNetVanilla
  class pointcloud_encoder(nn.Module):
      def __init__(self,mlp_dims,fc_dims,mlp_dolastReLU=False):
          assert(mlp_dims[-1]==fc_dims[0])
          super(pointcloud_encoder,self).__init__()
          self.PointNet=PointNetVanilla(mlp_dims,fc_dims,mlp_dolastReLU)
  
      def forward(self,x):
          # B*N*3
          f=self.PointNet.forward(x)  # B*K
          f=f.unsqueeze(1)  # B*1*512
          return f
  
  
  if __name__ == '__main__':
      b,n=64,1024
      x = torch.ones((b,n,3))
      MLP_dims = (3, 64, 64, 64, 128, 1024)
      FC_dims = (1024, 512, 512)
      model=pointcloud_encoder(MLP_dims,FC_dims)
      out=model(x)
      print(out.shape)