code: def get_memsize(svc):
      """Calculates the memory occupied by the parameters of an SVC object
  
      Parameters
      ----------
      svc : cuML SVC classifier object
  
      Return
      ------
      The GPU memory usage in bytes.
      """
      ms = 0
      for a in ["dual_coef_", "support_", "support_vectors_"]:
          x = getattr(svc, a)
          ms += np.prod(x[0].shape) * x[0].dtype.itemsize
      return ms
  
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 12 for calling a global function with example usage: get_memsize(svc) and returns: ms