code: def load_edges_from_disk(
      parquet_path, replication_factor, input_meta
  ):
      """
      Load the edges from disk into a graph data dictionary.
      Args:
          parquet_path: Path to the parquet directory.
          replication_factor: Number of times to replicate the edges.
          input_meta: Input meta data.
      Returns:
          dict: Dictionary of edge types to a tuple of (src, dst)
      """
      graph_data = {}
      for edge_type in input_meta["num_edges"].keys():
          print(
              f"Loading edge index for edge type {edge_type}"
              f"for replication factor = {replication_factor}"
          )
          can_edge_type = tuple(edge_type.split("__"))
          # TODO: Rename `edge_index` to a better name
          ei = pd.read_parquet(
              os.path.join(
                  parquet_path, edge_type, "edge_index.parquet"
              )
          )
          ei = {
              "src": torch.from_numpy(ei.src.values),
              "dst": torch.from_numpy(ei.dst.values),
          }
          if replication_factor > 1:
              src_ls = [ei["src"]]
              dst_ls = [ei["dst"]]
              for r in range(1, replication_factor):
                  new_src = ei["src"] + (
                      r * input_meta["num_nodes"][can_edge_type[0]]
                  )
                  src_ls.append(new_src)
                  new_dst = ei["dst"] + (
                      r * input_meta["num_nodes"][can_edge_type[2]]
                  )
                  dst_ls.append(new_dst)
  
              ei["src"] = torch.cat(src_ls).contiguous()
              ei["dst"] = torch.cat(dst_ls).contiguous()
          graph_data[can_edge_type] = ei["src"], ei["dst"]
      print("Graph Data compiled")
      return graph_data
  
file_path: cugraph/benchmarks/cugraph-dgl/python-script/dgl_dataloading_benchmark/dgl_benchmark.py
imports: import dgl
  import torch
  import pandas
  import os
  import time
  import json
  import random
  import numpy
  from argparse import ArgumentParser
label_desc: Use image node_id 1 for calling a global function with example usage: load_edges_from_disk(parquet_path, replication_factor, input_meta) and returns: graph_data