code: def __init__(self, out_channels, k=20, aggr="max"):
      super().__init__()
  
      self.conv1 = DynamicEdgeConv(MLP([2 * 3, 64, 64, 64]), k, aggr)
      self.conv2 = DynamicEdgeConv(MLP([2 * 64, 128]), k, aggr)
      self.lin1 = Linear(128 + 64, 1024)
  
      self.mlp = MLP(
          [1024, 512, 256, out_channels], dropout=0.5, norm=None
      )
  
file_path: pytorch_geometric/examples/dgcnn_classification.py
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, DynamicEdgeConv, global_max_pool
label_desc: Use image node_id 1 to create a new Net object from inherited base classes: torch.nn with example: obj = Net(out_channels, k, aggr)