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)).to(pos.get_device())
  
      out_x = []
      out_pos = []
      out_batch = []
  
      # 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)
  
      # save outputs for skipping connections
      out_x.append(x)
      out_pos.append(pos)
      out_batch.append(batch)
  
      # backbone down : #reduce cardinality and augment dimensionnality
      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)
  
          out_x.append(x)
          out_pos.append(pos)
          out_batch.append(batch)
  
      # summit
      x = self.mlp_summit(x)
      edge_index = knn_graph(pos, k=self.k, batch=batch)
      x = self.transformer_summit(x, pos, edge_index)
  
      # backbone up : augment cardinality and reduce dimensionnality
      n = len(self.transformers_down)
      for i in range(n):
          x = self.transition_up[-i - 1](
              x=out_x[-i - 2],
              x_sub=x,
              pos=out_pos[-i - 2],
              pos_sub=out_pos[-i - 1],
              batch_sub=out_batch[-i - 1],
              batch=out_batch[-i - 2],
          )
  
          edge_index = knn_graph(
              out_pos[-i - 2], k=self.k, batch=out_batch[-i - 2]
          )
          x = self.transformers_up[-i - 1](
              x, out_pos[-i - 2], edge_index
          )
  
      # 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 point_transformer_classification import TransformerBlock, TransitionDown
  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_graph, 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(x, pos, batch) and returns: F