code: def compare_svr(svr1, svr2, X_test, y_test, tol=1e-3):
      if X_test.shape[0] > 1:
          score1 = svr1.score(X_test, y_test)
          score2 = svr2.score(X_test, y_test)
          assert abs(score1 - score2) < tol
      else:
          y_pred1 = svr1.predict(X_test)
          y_pred2 = svr2.predict(X_test)
          mse1 = mean_squared_error(y_test, y_pred1)
          mse2 = mean_squared_error(y_test, y_pred2)
          assert (mse1 - mse2) / mse2 < tol
  
imports: import platform
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error
  from sklearn.datasets import make_classification, make_gaussian_quantiles
  from sklearn.datasets import make_regression, make_friedman1
  from sklearn.datasets import load_iris, make_blobs
  from sklearn import svm
  from cuml.testing.utils import unit_param, quality_param, stress_param, compare_svm, compare_probabilistic_svm, svm_array_equal
  from cuml.common import input_to_cuml_array
  import cuml.svm
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 16 for calling a global function with example usage: compare_svr(svr1, svr2, X_test, y_test, tol) without return types