code: def transform_after_split(datasets):
      """
      Dataset transformation after train/val/test split
      :param dataset: A list of DeepSNAP dataset objects
      :return: A list of transformed DeepSNAP dataset objects
      """
      if cfg.dataset.transform == "ego":
          for split_dataset in datasets:
              split_dataset.apply_transform(
                  ego_nets,
                  radius=cfg.gnn.layers_mp,
                  update_tensor=True,
                  update_graph=False,
              )
      elif cfg.dataset.transform == "edge":
          for split_dataset in datasets:
              split_dataset.apply_transform(
                  edge_nets, update_tensor=True, update_graph=False
              )
              split_dataset.task = "node"
          cfg.dataset.task = "node"
      return datasets
  
imports: import logging
  import pickle
  import time
  import networkx
  import torch
  import torch_geometric.transforms
  from deepsnap.batch import Batch
  from deepsnap.dataset import GraphDataset
  from ogb.graphproppred import PygGraphPropPredDataset
  from torch.utils.data import DataLoader
  from torch_geometric.datasets import PPI, Amazon, Coauthor, KarateClub, MNISTSuperpixels, Planetoid, QM7b, TUDataset
  import graphgym.models.feature_augment
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.models.transform import edge_nets, ego_nets, path_len, remove_node_feature
label_desc: Use image node_id 6 for calling a global function with example usage: transform_after_split(datasets) and returns: datasets