code: def __init__(self, num_classes):
      super().__init__()
  
      nn = Seq(Lin(3, 25), ReLU(), Lin(25, 1 * 64))
      self.conv1 = NNConv(1, 64, nn, aggr="mean")
  
      nn = Seq(Lin(3, 25), ReLU(), Lin(25, 64 * 64))
      self.conv2 = NNConv(64, 64, nn, aggr="mean")
  
      nn = Seq(Lin(3, 25), ReLU(), Lin(25, 64 * 128))
      self.conv3 = NNConv(64, 128, nn, aggr="mean")
  
      self.lin1 = torch.nn.Linear(128, 256)
      self.lin2 = torch.nn.Linear(256, 256)
      self.lin3 = torch.nn.Linear(256, num_classes)
  
file_path: pytorch_geometric/benchmark/points/mpnn.py
imports: import argparse
  import torch
  import torch.nn.functional
  from points.datasets import get_dataset
  from points.train_eval import run
  from torch.nn import Linear
  from torch.nn import ReLU
  from torch.nn import Sequential
  from torch_geometric.nn import NNConv, fps, global_mean_pool, radius_graph
  from torch_geometric.profile import rename_profile_file
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net(num_classes)