class_docstr: PglLinkPropPredDataset
      
docstr: Train/Validation/Test split
          
docstr_tok: ["Train\/Validation\/Test","split"]
code: def get_edge_split(self):
      """Train/Validation/Test split"""
      split_type = self.meta_info[self.name]["split"]
      path = osp.join(self.root, "split", split_type)
  
      train_idx = pd.read_csv(
          osp.join(path, "train.csv.gz"),
          compression="gzip",
          header=None,
      ).values
      valid_idx = pd.read_csv(
          osp.join(path, "valid.csv.gz"),
          compression="gzip",
          header=None,
      ).values
      test_idx = pd.read_csv(
          osp.join(path, "test.csv.gz"), compression="gzip", header=None
      ).values
  
      if self.task_type == "link prediction":
          target_type = np.int64
      else:
          target_type = np.float32
  
      return {
          "train_edge": np.array(train_idx[:, :2], dtype="int64"),
          "train_edge_label": np.array(
              train_idx[:, 2], dtype=target_type
          ),
          "valid_edge": np.array(valid_idx[:, :2], dtype="int64"),
          "valid_edge_label": np.array(
              valid_idx[:, 2], dtype=target_type
          ),
          "test_edge": np.array(test_idx[:, :2], dtype="int64"),
          "test_edge_label": np.array(
              test_idx[:, 2], dtype=target_type
          ),
      }
  
file_path: PGL/legacy/pgl/contrib/ogb/linkproppred/dataset_pgl.py
imports: import pandas
  import shutil, os
  import os.path
  import numpy
  from ogb.utils.url import decide_download, download_url, extract_zip
  from ogb.linkproppred import make_master_file
  from pgl.contrib.ogb.io.read_graph_pgl import read_csv_graph_pgl
label_desc: Use image node_id 3 for calling the PglLinkPropPredDataset obj's underlying member method code with example usage: obj.get_edge_split() and returns: dict