code: def forward(self, pos, batch):
      x = F.relu(self.conv1(None, pos, batch))
  
      idx = fps(pos, batch, ratio=0.375)
      x, pos, batch = x[idx], pos[idx], batch[idx]
  
      x = F.relu(self.conv2(x, pos, batch))
  
      idx = fps(pos, batch, ratio=0.334)
      x, pos, batch = x[idx], pos[idx], batch[idx]
  
      x = F.relu(self.conv3(x, pos, batch))
      x = F.relu(self.conv4(x, pos, batch))
  
      x = global_mean_pool(x, batch)
  
      x = F.relu(self.lin1(x))
      x = F.relu(self.lin2(x))
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin3(x)
      return F.log_softmax(x, dim=-1)
  
file_path: pytorch_geometric/benchmark/points/point_cnn.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_geometric.nn import XConv, fps, global_mean_pool
  from torch_geometric.profile import rename_profile_file
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(pos, batch) and returns: F