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} for replication factor = {replication_factor}"
          )
  
          canonical_edge_type = tuple(edge_type.split("__"))
          edge_index = pd.read_parquet(
              os.path.join(
                  parquet_path, edge_type, "edge_index.parquet"
              )
          )
          edge_index = {
              "src": torch.from_numpy(edge_index.src.values),
              "dst": torch.from_numpy(edge_index.dst.values),
          }
  
          if replication_factor > 1:
              src_list, dst_list = replicate_edges(
                  edge_index,
                  canonical_edge_type,
                  replication_factor,
                  input_meta,
              )
              edge_index["src"] = torch.cat(src_list).contiguous()
              edge_index["dst"] = torch.cat(dst_list).contiguous()
  
          graph_data[canonical_edge_type] = (
              edge_index["src"],
              edge_index["dst"],
          )
  
      print("Read Edge Data")
      return graph_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 1 for calling a global function with example usage: load_edges_from_disk(parquet_path, replication_factor, input_meta) and returns: graph_data