text: import torch.nn as nn
  import torch.nn.functional as F
  
  from cogdl.layers import MixHopLayer
  
  from .. import BaseModel
  
  
  class MixHop(BaseModel):
      @staticmethod
      def add_args(parser):
          """Add model-specific arguments to the parser."""
          # fmt: off
          parser.add_argument("--num-features", type=int)
          parser.add_argument("--num-classes", type=int)
          parser.add_argument("--dropout", type=float, default=0.7)
          parser.add_argument("--layer1-pows", type=int, nargs="+", default=[200, 200, 200])
          parser.add_argument("--layer2-pows", type=int, nargs="+", default=[20, 20, 20])
          # fmt: on
  
      @classmethod
      def build_model_from_args(cls, args):
          return cls(
              args.num_features,
              args.num_classes,
              args.dropout,
              args.layer1_pows,
              args.layer2_pows,
          )
  
      def __init__(self, num_features, num_classes, dropout, layer1_pows, layer2_pows):
          super(MixHop, self).__init__()
  
          self.dropout = dropout
  
          self.num_features = num_features
          self.num_classes = num_classes
          self.dropout = dropout
          layer_pows = [layer1_pows, layer2_pows]
  
          shapes = [num_features] + [sum(layer1_pows), sum(layer2_pows)]
  
          self.mixhops = nn.ModuleList(
              [MixHopLayer(shapes[layer], [0, 1, 2], layer_pows[layer]) for layer in range(len(layer_pows))]
          )
          self.fc = nn.Linear(shapes[-1], num_classes)
  
      def forward(self, graph):
          x = graph.x
          for mixhop in self.mixhops:
              x = F.relu(mixhop(graph, x))
              x = F.dropout(x, p=self.dropout, training=self.training)
          x = self.fc(x)
          return x
  
      def predict(self, data):
          return self.forward(data)
  