code: def _graph_kernels_load(
      dataset,
      n_graphs,
      total_nodes,
      max_nodes,
      mean_nodes,
      total_edges,
      mean_edges,
      expected_labels,
      node_features,
  ):
      graphs, labels = dataset.load()
  
      assert len(graphs) == n_graphs
      assert len(labels) == n_graphs
  
      n_nodes = [g.number_of_nodes() for g in graphs]
      assert sum(n_nodes) == total_nodes
      assert max(n_nodes) == max_nodes
  
      n_edges = [g.number_of_edges() for g in graphs]
      assert sum(n_edges) == total_edges
  
      # verify that the numbers we've written match the averages from the table in
      # https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets
      assert np.mean(n_nodes) == pytest.approx(mean_nodes, 0.005)
      # directed -> undirected doubles the number of edges
      assert np.mean(n_edges) == pytest.approx(mean_edges * 2, 0.005)
  
      assert set(labels) == expected_labels
  
      # all graphs should be homogeneous
      node_labels = {tuple(g.node_types) for g in graphs}
      assert node_labels == {("default",)}
  
      feature_sizes = {
          g.node_feature_sizes()["default"] for g in graphs
      }
      assert feature_sizes == {node_features}
  
imports: import pytest
  import tempfile
  import os
  import sys
  import numpy
  from stellargraph.datasets import *
  from urllib.error import URLError
  from stellargraph.datasets.dataset_loader import DatasetLoader
  from urllib.request import urlretrieve
  from unittest.mock import patch
label_desc: Use image node_id 7 for calling a global function with example usage: _graph_kernels_load(dataset, n_graphs, total_nodes, max_nodes, mean_nodes, total_edges, mean_edges, expected_labels, node_features) without return types