code: def test_eval_loader_kwargs(get_dataset):
      data = get_dataset(name="Cora")[0]
  
      node_sampler = NeighborSampler(data, num_neighbors=[5])
  
      datamodule = LightningNodeData(
          data,
          node_sampler=node_sampler,
          batch_size=32,
          eval_loader_kwargs=dict(num_neighbors=[-1], batch_size=64),
      )
  
      assert datamodule.loader_kwargs["batch_size"] == 32
      assert datamodule.graph_sampler.num_neighbors.values == [5]
      assert datamodule.eval_loader_kwargs["batch_size"] == 64
      assert datamodule.eval_graph_sampler.num_neighbors.values == [-1]
  
      train_loader = datamodule.train_dataloader()
      assert math.ceil(int(data.train_mask.sum()) / 32) == len(
          train_loader
      )
  
      val_loader = datamodule.val_dataloader()
      assert math.ceil(int(data.val_mask.sum()) / 64) == len(val_loader)
  
      test_loader = datamodule.test_dataloader()
      assert math.ceil(int(data.test_mask.sum()) / 64) == len(
          test_loader
      )
  
      pred_loader = datamodule.predict_dataloader()
      assert math.ceil(data.num_nodes / 64) == len(pred_loader)
  
imports: import math
  import pytest
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.data.lightning import LightningDataset, LightningLinkData, LightningNodeData
  from torch_geometric.nn import global_mean_pool
  from torch_geometric.sampler import BaseSampler, NeighborSampler
  from torch_geometric.testing import MyFeatureStore, MyGraphStore, get_random_edge_index, onlyCUDA, onlyFullTest, onlyNeighborSampler, onlyOnline, withPackage
label_desc: Use image node_id 7 for calling a global function with example usage: test_eval_loader_kwargs(get_dataset) without return types