class_docstr_tok: []
code: def transform(self, data):
      if data.x is None:
          data.x = torch.ones([data.num_nodes, 1], dtype=torch.float)
  
      # ignore edge noises
  
      deg, deg_onehot = self.compute_degree(
          data.edge_index, data.num_nodes
      )
      akx = self.compute_akx(data.num_nodes, data.x, data.edge_index)
      cent = self.compute_centrality(data)
      # data.x = torch.cat([data.x, deg, deg_onehot, akx, cent], dim=-1)
      # data.x = torch.cat([data.x, deg_onehot, akx, cent, deg], dim=-1)
      data.x = torch.cat([deg, data.x, deg_onehot, akx, cent], dim=-1)
  
      return data
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.utils import degree, remove_self_loops, add_self_loops
label_desc: Use image node_id 2 for calling the FeatureExpander obj's underlying member method code with example usage: obj.transform(data) and returns: data