code: def test_svm_predict(params, n_pred):
      n_rows = 500
      n_cols = 2
      X, y = make_blobs(
          n_samples=n_rows + n_pred,
          n_features=n_cols,
          centers=[[-5, -5], [5, 5]],
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=n_rows
      )
      cuSVC = cu_svm.SVC(**params)
      cuSVC.fit(X_train, y_train)
      y_pred = cuSVC.predict(X_test)
      n_correct = np.sum(y_test == y_pred)
      accuracy = n_correct * 100 / n_pred
      assert accuracy > 99
  
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 7 for calling a global function with example usage: test_svm_predict(params, n_pred) without return types