code: def test_1d_custom_ranks_incorrect_features(
      ploomber_value_error_message,
  ):
      ranks = np.array([0.96784323, 0.79394983, 0.47927427])
      with pytest.raises(
          ValueError, match=ploomber_value_error_message
      ) as e:
          Rank1D(
              features=["Feature1", "Feature2", "Feature3", "Feature4"]
          ).feature_ranks_custom_algorithm(ranks)
          assert (
              "number of supplied feature names does not match the number "
              "of ranks provided." in str(e.value)
          )
  
imports: import matplotlib.pyplot
  import pytest
  import pandas
  import numpy
  from sklearn.datasets import load_iris
  from matplotlib.testing.decorators import image_comparison
  from sklearn_evaluation.plot import Rank1D, Rank2D
label_desc: Use image node_id 10 for calling a global function with example usage: test_1d_custom_ranks_incorrect_features(ploomber_value_error_message) without return types