code: def test_svm_gamma(params):
      # Note: we test different array types to make sure that the X.var() is
      # calculated correctly for gamma == 'scale' option.
      x_arraytype = params.pop("x_arraytype", "numpy")
      n_rows = 500
      n_cols = 380
      centers = [10 * np.ones(380), -10 * np.ones(380)]
      X, y = make_blobs(
          n_samples=n_rows,
          n_features=n_cols,
          random_state=137,
          centers=centers,
      )
      X = X.astype(np.float32)
      if x_arraytype == "dataframe":
          y = cudf.Series(y)
      elif x_arraytype == "numba":
          X = cuda.to_device(X)
      # Using degree 40 polynomials and fp32 training would fail with
      # gamma = 1/(n_cols*X.std()), but it works with the correct implementation:
      # gamma = 1/(n_cols*X.var())
      cuSVC = cu_svm.SVC(**params)
      cuSVC.fit(X, y)
      accuracy = cuSVC.score(X, y) * 100
      assert accuracy > 70
  
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 10 for calling a global function with example usage: test_svm_gamma(params) without return types