code: def __init__(self, in_channels, out_channels):
      super().__init__()
      self.lin_in = Lin(in_channels, in_channels)
      self.lin_out = Lin(out_channels, out_channels)
  
      self.pos_nn = MLP(
          [3, 64, out_channels], norm=None, plain_last=False
      )
  
      self.attn_nn = MLP(
          [out_channels, 64, out_channels], norm=None, plain_last=False
      )
  
      self.transformer = PointTransformerConv(
          in_channels,
          out_channels,
          pos_nn=self.pos_nn,
          attn_nn=self.attn_nn,
      )
  
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 TransformerBlock object from inherited base classes: torch.nn with example: obj = TransformerBlock(in_channels, out_channels)