code: def forward(self, data):
      x, pos, batch = data.x, data.pos, data.batch
      x0 = torch.cat([x, pos], dim=-1)
      x1 = self.conv1(x0, batch)
      x2 = self.conv2(x1, batch)
      x3 = self.conv3(x2, batch)
      out = self.mlp(torch.cat([x1, x2, x3], dim=1))
      return F.log_softmax(out, dim=1)
  
file_path: pytorch_geometric/examples/dgcnn_segmentation.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torchmetrics.functional import jaccard_index
  import torch_geometric.transforms
  from torch_geometric.datasets import ShapeNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, DynamicEdgeConv
  from torch_geometric.utils import scatter
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