code: def forward(self, data):
      pos, batch = data.pos, data.batch
      x1 = self.conv1(pos, batch)
      x2 = self.conv2(x1, batch)
      out = self.lin1(torch.cat([x1, x2], dim=1))
      out = global_max_pool(out, batch)
      out = self.mlp(out)
      return F.log_softmax(out, dim=1)
  
file_path: pytorch_geometric/examples/dgcnn_classification.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  import torch_geometric.transforms
  from torch_geometric.datasets import ModelNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, DynamicEdgeConv, global_max_pool
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(data) and returns: F