code: def test_eigenvector_centrality_toy(graph_file):
      # This test is based off of libcugraph_c and pylibcugraph tests
      G = graph_file.get_graph(
          download=True, create_using=cugraph.Graph(directed=True)
      )
  
      tol = 1e-6
      max_iter = 200
      centralities = [
          0.236325,
          0.292055,
          0.458457,
          0.60533,
          0.190498,
          0.495942,
      ]
  
      ck = cugraph.eigenvector_centrality(G, tol=tol, max_iter=max_iter)
  
      ck = ck.sort_values("vertex")
      for vertex in ck["vertex"].to_pandas():
          expected_score = centralities[vertex]
          actual_score = ck["eigenvector_centrality"].iloc[vertex]
          assert (
              pytest.approx(expected_score, abs=1e-4) == actual_score
          ), (
              f"Eigenvector centrality score is {actual_score}, should have"
              f" been {expected_score}"
          )
  
file_path: cugraph/python/cugraph/cugraph/tests/centrality/test_eigenvector_centrality.py
imports: import gc
  import pytest
  import networkx
  import cugraph
  from cugraph.testing import utils, UNDIRECTED_DATASETS, DEFAULT_DATASETS
  from cugraph.datasets import toy_graph, karate
label_desc: Use image node_id 6 for calling a global function with example usage: test_eigenvector_centrality_toy(graph_file) without return types