text: import argparse
  import torch
  import torch.nn.functional as F
  from torch.nn import Linear as Lin
  from scripts.study_case.ID_4.torch_geometric.nn import SplineConv, radius_graph, fps, global_mean_pool
  
  from points.datasets import get_dataset
  from points.train_eval import run
  
  parser = argparse.ArgumentParser()
  parser.add_argument('--epochs', type=int, default=200)
  parser.add_argument('--batch_size', type=int, default=8)
  parser.add_argument('--lr', type=float, default=0.001)
  parser.add_argument('--lr_decay_factor', type=float, default=0.5)
  parser.add_argument('--lr_decay_step_size', type=int, default=50)
  parser.add_argument('--weight_decay', type=float, default=0)
  args = parser.parse_args()
  
  
  class Net(torch.nn.Module):
      def __init__(self, num_classes):
          super(Net, self).__init__()
  
          self.conv1 = SplineConv(1, 64, dim=3, kernel_size=5)
          self.conv2 = SplineConv(64, 64, dim=3, kernel_size=5)
          self.conv3 = SplineConv(64, 128, dim=3, kernel_size=5)
  
          self.lin1 = Lin(128, 256)
          self.lin2 = Lin(256, 256)
          self.lin3 = Lin(256, num_classes)
  
      def forward(self, pos, batch):
          x = pos.new_ones((pos.size(0), 1))
  
          radius = 0.2
          edge_index = radius_graph(pos, r=radius, batch=batch)
          pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
          pseudo = pseudo.clamp(min=0, max=1)
          x = F.elu(self.conv1(x, edge_index, pseudo))
  
          idx = fps(pos, batch, ratio=0.5)
          x, pos, batch = x[idx], pos[idx], batch[idx]
  
          radius = 0.4
          edge_index = radius_graph(pos, r=radius, batch=batch)
          pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
          pseudo = pseudo.clamp(min=0, max=1)
          x = F.elu(self.conv2(x, edge_index, pseudo))
  
          idx = fps(pos, batch, ratio=0.25)
          x, pos, batch = x[idx], pos[idx], batch[idx]
  
          radius = 1
          edge_index = radius_graph(pos, r=radius, batch=batch)
          pseudo = (pos[edge_index[1]] - pos[edge_index[0]]) / (2 * radius) + 0.5
          pseudo = pseudo.clamp(min=0, max=1)
          x = F.elu(self.conv3(x, edge_index, pseudo))
  
          x = global_mean_pool(x, batch)
  
          x = F.elu(self.lin1(x))
          x = F.elu(self.lin2(x))
          x = F.dropout(x, p=0.5, training=self.training)
          x = self.lin3(x)
          return F.log_softmax(x, dim=-1)
  
  
  train_dataset, test_dataset = get_dataset(num_points=1024)
  model = Net(train_dataset.num_classes)
  run(train_dataset, test_dataset, model, args.epochs, args.batch_size, args.lr,
      args.lr_decay_factor, args.lr_decay_step_size, args.weight_decay)
  