code: def __init__(self, in_feats, hidden, out_feats, R, n_layers, dropout):
      super(Model, self).__init__()
      self.dropout = nn.Dropout(dropout)
      self.prelu = nn.PReLU()
      self.inception_ffs = nn.ModuleList()
      for hop in range(R + 1):
          self.inception_ffs.append(
              FeedForwardNet(
                  in_feats, hidden, hidden, n_layers, dropout
              )
          )
      # self.linear = nn.Linear(hidden * (R + 1), out_feats)
      self.project = FeedForwardNet(
          (R + 1) * hidden, hidden, out_feats, n_layers, dropout
      )
  
imports: import argparse
  import os
  import time
  import dgl
  import dgl.function
  import torch
  import torch.nn
  import torch.nn.functional
  from dataset import load_dataset
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model(in_feats, hidden, out_feats, R, n_layers, dropout)