code: def svd_feature(features, d=200):
      """Get 200-dimensional node features, to avoid curse of dimensionality"""
      if features.shape[1] <= d:
          return features
      U, S, VT = torch.svd(features)
      res = torch.mm(U[:, 0:d], torch.diag(S[0:d]))
      return res
  
imports: import dgl
  import torch
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset
label_desc: Use image node_id 2 for calling a global function with example usage: svd_feature(features, d) and returns: res, features