code: def test_temporal_hetero_neighbor_loader_on_cora(get_dataset):
      dataset = get_dataset(name="Cora")
      data = dataset[0]
  
      hetero_data = HeteroData()
      hetero_data["paper"].x = data.x
      hetero_data["paper"].time = torch.arange(data.num_nodes, 0, -1)
      hetero_data["paper", "paper"].edge_index = data.edge_index
  
      loader = NeighborLoader(
          hetero_data,
          num_neighbors=[-1, -1],
          input_nodes="paper",
          time_attr="time",
          batch_size=1,
      )
  
      for batch in loader:
          mask = batch["paper"].time[0] >= batch["paper"].time[1:]
          assert torch.all(mask)
  
imports: import os.path
  import numpy
  import pytest
  import torch
  import torch_geometric.typing
  from torch_geometric import EdgeIndex
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import GraphConv, to_hetero
  from torch_geometric.sampler.base import SubgraphType
  from torch_geometric.testing import MyFeatureStore, MyGraphStore, get_random_edge_index, get_random_tensor_frame, onlyLinux, onlyNeighborSampler, onlyOnline, withCUDA, withPackage
  from torch_geometric.typing import WITH_EDGE_TIME_NEIGHBOR_SAMPLE, WITH_PYG_LIB, WITH_TORCH_SPARSE, WITH_WEIGHTED_NEIGHBOR_SAMPLE, TensorFrame
  from torch_geometric.utils import is_undirected, sort_edge_index, to_torch_csr_tensor, to_undirected
label_desc: Use image node_id 6 for calling a global function with example usage: test_temporal_hetero_neighbor_loader_on_cora(get_dataset) without return types