code: def load_node_features(dataset_path, replication_factor, node_type):
      print("Loading node features", flush=True)
      node_type_path = os.path.join(dataset_path, "npy", node_type)
      if replication_factor == 1:
          fname = os.path.join(node_type_path, "node_feat.npy")
      else:
          fname = os.path.join(
              node_type_path, f"node_feat_{replication_factor}x.npy"
          )
  
      feat = torch.from_numpy(np.load(fname))
      print("Loaded node features", flush=True)
      return feat
  
file_path: cugraph/benchmarks/cugraph-dgl/scale-benchmarks/load_graph_feats.py
imports: import numpy
  import pandas
  import torch
  import os
label_desc: Use image node_id 6 for calling a global function with example usage: load_node_features(dataset_path, replication_factor, node_type) and returns: feat