code: def test_neighbor_loader_mapping():
      edge_index = torch.tensor(
          [
              [0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 3, 5],
              [1, 2, 3, 4, 5, 8, 6, 7, 9, 10, 6, 11],
          ]
      )
      data = Data(edge_index=edge_index, num_nodes=12)
  
      loader = NeighborLoader(
          data,
          num_neighbors=[1],
          batch_size=2,
          shuffle=True,
      )
  
      for batch in loader:
          assert torch.equal(
              batch.n_id[batch.edge_index],
              data.edge_index[:, batch.e_id],
          )
  
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 15 for calling a global function with example usage: test_neighbor_loader_mapping() without return types