code: def forward(self, feats):
      feats = [self.input_drop(feat) for feat in feats]
      hidden = []
      for feat, ff in zip(feats, self.inception_ffs):
          hidden.append(ff(feat))
      out = self.project(
          self.dropout(self.prelu(torch.cat(hidden, dim=-1)))
      )
      return out
  
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 2 for calling the SIGN obj's underlying member method code with example usage: obj.forward(feats) and returns: out