code: def process_node_label(
      label_path,
      node_type,
      replication_factor,
      num_nodes_dict,
      input_meta,
  ):
      node_label = pd.read_parquet(label_path)
  
      if replication_factor > 1:
          node_label = replicate_node_label(
              node_label, node_type, replication_factor, input_meta
          )
  
      node_label_tensor = torch.full(
          (num_nodes_dict[node_type],), -1, dtype=torch.float32
      )
      node_label_tensor[
          torch.as_tensor(node_label.node.values)
      ] = torch.as_tensor(node_label.label.values)
  
      del node_label
  
      return {
          "train_idx": (node_label_tensor > -1)
          .contiguous()
          .nonzero()
          .view(-1),
          "y": node_label_tensor.contiguous().long(),
      }
  
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 4 for calling a global function with example usage: process_node_label(label_path, node_type, replication_factor, num_nodes_dict, input_meta) and returns: dict