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