code: def forward(self, data):
      sa0_out = (data.x, data.pos, data.batch)
      sa1_out = self.sa1_module(*sa0_out)
      sa2_out = self.sa2_module(*sa1_out)
      sa3_out = self.sa3_module(*sa2_out)
  
      fp3_out = self.fp3_module(*sa3_out, *sa2_out)
      fp2_out = self.fp2_module(*fp3_out, *sa1_out)
      x, _, _ = self.fp1_module(*fp2_out, *sa0_out)
  
      return self.mlp(x).log_softmax(dim=-1)
  
imports: import os.path
  import torch
  import torch.nn.functional
  from pointnet2_classification import GlobalSAModule, SAModule
  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, knn_interpolate
  from torch_geometric.typing import WITH_TORCH_CLUSTER
  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: self