code: def load_node_labels(dataset_path, replication_factor, input_meta):
      num_nodes_dict = {
          node_type: t * replication_factor
          for node_type, t in input_meta["num_nodes"].items()
      }
      node_data = {}
  
      for node_type in input_meta["num_nodes"].keys():
          node_data[node_type] = {}
          label_path = os.path.join(
              dataset_path, "parquet", node_type, "node_label.parquet"
          )
  
          if os.path.exists(label_path):
              node_data[node_type] = process_node_label(
                  label_path,
                  node_type,
                  replication_factor,
                  num_nodes_dict,
                  input_meta,
              )
  
          else:
              node_data[node_type]["num_nodes"] = num_nodes_dict[
                  node_type
              ]
  
      print("Loaded node labels", flush=True)
      return node_data
  
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 3 for calling a global function with example usage: load_node_labels(dataset_path, replication_factor, input_meta) and returns: node_data