code: def forward(self, x, pos, batch):
      edge_index = knn_graph(
          pos, self.num_neighbors, batch=batch, loop=True
      )
  
      shortcut_of_x = self.shortcut(x)  # N, d_out
      x = self.mlp1(x)  # N, d_out//8
      x = self.lfa1(edge_index, x, pos)  # N, d_out//2
      x = self.lfa2(edge_index, x, pos)  # N, d_out//2
      x = self.mlp2(x)  # N, d_out
      x = self.lrelu(x + shortcut_of_x)  # N, d_out
  
      return x, pos, batch
  
imports: import os.path
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Linear
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.datasets import ModelNet
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP
  from torch_geometric.nn.aggr import MaxAggregation
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.pool import knn_graph
  from torch_geometric.nn.pool.decimation import decimation_indices
  from torch_geometric.typing import WITH_TORCH_CLUSTER
  from torch_geometric.utils import softmax
label_desc: Use image node_id 2 for calling the DilatedResidualBlock obj's underlying member method code with example usage: obj.forward(x, pos, batch) and returns: x,  pos,  batch