code: def forward(self, x, edge_index):
      x = F.dropout(x, p=0.7, training=self.training)
  
      x = self.conv1(x, edge_index)
      x = self.norm1(x)
      x = F.dropout(x, p=0.9, training=self.training)
  
      x = self.conv2(x, edge_index)
      x = self.norm2(x)
      x = F.dropout(x, p=0.9, training=self.training)
  
      x = self.conv3(x, edge_index)
      x = self.norm3(x)
      x = F.dropout(x, p=0.9, training=self.training)
  
      return self.lin(x)
  
file_path: pytorch_geometric/examples/mixhop.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch_geometric.datasets import Planetoid
  from torch_geometric.nn import BatchNorm, Linear, MixHopConv
label_desc: Use image node_id 2 for calling the MixHop obj's underlying member method code with example usage: obj.forward(x, edge_index) and returns: self