code: def test_mse_diabetes(diabetes_split_dataset_and_model):
      # Arrange
      _, test_ds, clf = diabetes_split_dataset_and_model
      preds = clf.predict(test_ds.features_columns)
  
      # Act
      score = calculate_neg_mse_per_sample(test_ds.label_col, preds)
      score_sklearn = mean_squared_error(test_ds.label_col, preds)
  
      # Assert
      assert_that(score.mean(), close_to(-1 * 3296, 1))
      assert_that(score.mean(), close_to(-1 * score_sklearn, 0.01))
  
imports: import pandas
  from hamcrest import assert_that, calling, close_to, has_entries, is_, raises
  from sklearn.metrics import log_loss, make_scorer, mean_squared_error
  from deepchecks.core.errors import DeepchecksValueError
  from deepchecks.tabular import Dataset
  from deepchecks.tabular.metric_utils import DeepcheckScorer
  from deepchecks.tabular.metric_utils.additional_classification_metrics import false_negative_rate_metric, false_positive_rate_metric, true_negative_rate_metric
  from deepchecks.tabular.utils.task_inference import get_all_labels, infer_classes_from_model
  from deepchecks.utils.single_sample_metrics import calculate_neg_cross_entropy_per_sample, calculate_neg_mse_per_sample
  from tests.common import is_nan
label_desc: Use image node_id 13 for calling a global function with example usage: test_mse_diabetes(diabetes_split_dataset_and_model) without return types