code: def create_loader():
      """
      Create data loader object
  
      Returns: List of PyTorch data loaders
  
      """
      dataset = create_dataset()
      # train loader
      if cfg.dataset.task == "graph":
          id = dataset.data["train_graph_index"]
          loaders = [
              get_loader(
                  dataset[id],
                  cfg.train.sampler,
                  cfg.train.batch_size,
                  shuffle=True,
              )
          ]
          delattr(dataset.data, "train_graph_index")
      else:
          loaders = [
              get_loader(
                  dataset,
                  cfg.train.sampler,
                  cfg.train.batch_size,
                  shuffle=True,
              )
          ]
  
      # val and test loaders
      for i in range(cfg.share.num_splits - 1):
          if cfg.dataset.task == "graph":
              split_names = ["val_graph_index", "test_graph_index"]
              id = dataset.data[split_names[i]]
              loaders.append(
                  get_loader(
                      dataset[id],
                      cfg.val.sampler,
                      cfg.train.batch_size,
                      shuffle=False,
                  )
              )
              delattr(dataset.data, split_names[i])
          else:
              loaders.append(
                  get_loader(
                      dataset,
                      cfg.val.sampler,
                      cfg.train.batch_size,
                      shuffle=False,
                  )
              )
  
      return loaders
  
file_path: GraphGym/graphgym/loader_pyg.py
imports: from typing import Callable
  import torch
  import torch_geometric.transforms
  from torch_geometric.datasets import PPI, Amazon, Coauthor, KarateClub, MNISTSuperpixels, Planetoid, QM7b, TUDataset
  from torch_geometric.loader import ClusterLoader, DataLoader, GraphSAINTEdgeSampler, GraphSAINTNodeSampler, GraphSAINTRandomWalkSampler, NeighborSampler, RandomNodeSampler
  from torch_geometric.utils import index_to_mask, negative_sampling, to_undirected
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.models.transform import create_link_label, neg_sampling_transform
label_desc: Use image node_id 9 for calling a global function with example usage: create_loader() and returns: loaders