code: def __init__(self, in_channels, out_channels, dim_model, k=16):
      super().__init__()
      self.k = k
  
      # dummy feature is created if there is none given
      in_channels = max(in_channels, 1)
  
      # first block
      self.mlp_input = MLP(
          [in_channels, dim_model[0]], plain_last=False
      )
  
      self.transformer_input = TransformerBlock(
          in_channels=dim_model[0], out_channels=dim_model[0]
      )
      # backbone layers
      self.transformers_down = torch.nn.ModuleList()
      self.transition_down = torch.nn.ModuleList()
  
      for i in range(len(dim_model) - 1):
          # Add Transition Down block followed by a Transformer block
          self.transition_down.append(
              TransitionDown(
                  in_channels=dim_model[i],
                  out_channels=dim_model[i + 1],
                  k=self.k,
              )
          )
  
          self.transformers_down.append(
              TransformerBlock(
                  in_channels=dim_model[i + 1],
                  out_channels=dim_model[i + 1],
              )
          )
  
      # class score computation
      self.mlp_output = MLP(
          [dim_model[-1], 64, out_channels], norm=None
      )
  
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 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net(in_channels, out_channels, dim_model, k)