code: def compare_probabilistic_svm(
      svc1, svc2, X_test, y_test, tol=1e-3, brier_tol=1e-3
  ):
      """Compare the probability output from two support vector classifiers."""
  
      prob1 = svc1.predict_proba(X_test)
      prob2 = svc2.predict_proba(X_test)
      assert mean_squared_error(prob1, prob2) <= tol
  
      if svc1.n_classes_ == 2:
          brier1 = brier_score_loss(y_test, prob1[:, 1])
          brier2 = brier_score_loss(y_test, prob2[:, 1])
          # Brier score - smaller is better
          assert brier1 - brier2 <= brier_tol
  
imports: import pytest
  from cuml.internals.mem_type import MemoryType
  from cuml.internals.input_utils import input_to_cuml_array, is_array_like
  from cuml.internals.base import Base
  import cuml
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error, brier_score_loss
  from sklearn.datasets import make_classification, make_regression
  from sklearn import datasets
  from pylibraft.common.cuda import Stream
  from sklearn.datasets import make_regression
  from numba.cuda.cudadrv.devicearray import DeviceNDArray
  from numbers import Number
  from cuml.internals.safe_imports import gpu_only_import_from
  from itertools import dropwhile
  from copy import deepcopy
  from cuml.internals.safe_imports import cpu_only_import
  import inspect
  from textwrap import dedent, indent
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 24 for calling a global function with example usage: compare_probabilistic_svm(svc1, svc2, X_test, y_test, tol, brier_tol) without return types