code: def __init__(
      self,
      num_neighbors,
      d_in: int,
      d_out: int,
  ):
      super().__init__()
      self.num_neighbors = num_neighbors
      self.d_in = d_in
      self.d_out = d_out
  
      # MLP on input
      self.mlp1 = SharedMLP([d_in, d_out // 8])
      # MLP on input, and the result is summed with the output of mlp2
      self.shortcut = SharedMLP([d_in, d_out], act=None)
      # MLP on output
      self.mlp2 = SharedMLP([d_out // 2, d_out], act=None)
  
      self.lfa1 = LocalFeatureAggregation(d_out // 4)
      self.lfa2 = LocalFeatureAggregation(d_out // 2)
  
      self.lrelu = torch.nn.LeakyReLU(**lrelu02_kwargs)
  
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 1 to create a new DilatedResidualBlock object from inherited base classes: torch.nn with example: obj = DilatedResidualBlock(num_neighbors, d_in, d_out)