code: def forward(self, x, pos, batch=None):
      # add dummy features in case there is none
      if x is None:
          x = torch.ones((pos.shape[0], 1), device=pos.get_device())
  
      # first block
      x = self.mlp_input(x)
      edge_index = knn_graph(pos, k=self.k, batch=batch)
      x = self.transformer_input(x, pos, edge_index)
  
      # backbone
      for i in range(len(self.transformers_down)):
          x, pos, batch = self.transition_down[i](x, pos, batch=batch)
  
          edge_index = knn_graph(pos, k=self.k, batch=batch)
          x = self.transformers_down[i](x, pos, edge_index)
  
      # GlobalAveragePooling
      x = global_mean_pool(x, batch)
  
      # Class score
      out = self.mlp_output(x)
  
      return F.log_softmax(out, dim=-1)
  
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, PointTransformerConv, fps, global_mean_pool, knn, knn_graph
  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(x, pos, batch) and returns: F