code: def test_svm_numeric_arraytype(x_dtype, y_dtype):
      X, y = get_binary_iris_dataset()
      X = X.astype(x_dtype, order="F")
      y = y.astype(y_dtype)
  
      params = {"kernel": "rbf", "C": 1, "gamma": 0.25}
      cuSVC = cu_svm.SVC(**params)
      cuSVC.fit(X, y)
      intercept_exp = 0.23468959692060373
      n_sv_exp = 15
      assert (
          abs(cuSVC.intercept_ - intercept_exp) / intercept_exp < 1e-3
      )
      assert cuSVC.n_support_ == n_sv_exp
      n_pred_wrong = np.sum(cuSVC.predict(X) - y)
      assert n_pred_wrong == 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 11 for calling a global function with example usage: test_svm_numeric_arraytype(x_dtype, y_dtype) without return types