code: def test_random_link_split_on_dataset(get_dataset):
      dataset = get_dataset(name="MUTAG")
  
      dataset.transform = RandomLinkSplit(
          num_val=0.1,
          num_test=0.1,
          disjoint_train_ratio=0.3,
          add_negative_train_samples=False,
      )
  
      train_dataset, val_dataset, test_dataset = zip(*dataset)
      assert len(train_dataset) == len(dataset)
      assert len(val_dataset) == len(dataset)
      assert len(test_dataset) == len(dataset)
  
      assert isinstance(train_dataset[0], Data)
      assert train_dataset[0].edge_label.min() == 1.0
      assert train_dataset[0].edge_label.max() == 1.0
  
      assert isinstance(val_dataset[0], Data)
      assert val_dataset[0].edge_label.min() == 0.0
      assert val_dataset[0].edge_label.max() == 1.0
  
      assert isinstance(test_dataset[0], Data)
      assert test_dataset[0].edge_label.min() == 0.0
      assert test_dataset[0].edge_label.max() == 1.0
  
file_path: pytorch_geometric/test/transforms/test_random_link_split.py
imports: import pytest
  import torch
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.testing import get_random_edge_index, onlyFullTest, onlyOnline
  from torch_geometric.transforms import RandomLinkSplit
  from torch_geometric.utils import is_undirected, to_undirected
label_desc: Use image node_id 8 for calling a global function with example usage: test_random_link_split_on_dataset(get_dataset) without return types