code: def test_svm_no_support_vectors():
      n_rows = 10
      n_cols = 3
      X = cp.random.uniform(size=(n_rows, n_cols), dtype=cp.float64)
      y = cp.ones((n_rows, 1))
      model = cuml.svm.SVR(kernel="linear", C=10)
      model.fit(X, y)
      pred = model.predict(X)
  
      assert svm_array_equal(pred, y, 0)
  
      assert model.n_support_ == 0
      assert abs(model.intercept_ - 1) <= 1e-6
      assert svm_array_equal(model.coef_, cp.zeros((1, n_cols)))
      assert model.dual_coef_.shape == (1, 0)
      assert model.support_.shape == (0,)
      assert model.support_vectors_.shape[0] == 0
  
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 20 for calling a global function with example usage: test_svm_no_support_vectors() without return types