code: def test_validate_calibration_params_valid_parameters(
      estimator, _, valid_args
  ):
      # test that no warning message is returned if valid arguments are given to
      # _validate_calibration_params for all pairs metric learners, as well as
      # a mocking example, and the class itself
      with pytest.warns(None) as record:
          estimator._validate_calibration_params(**valid_args)
      assert len(record) == 0
  
imports: from functools import partial
  import pytest
  from numpy.testing import assert_array_equal
  from scipy.spatial.distance import euclidean
  from metric_learn.base_metric import _PairsClassifierMixin, MahalanobisMixin
  from sklearn.exceptions import NotFittedError
  from sklearn.metrics import f1_score, accuracy_score, fbeta_score, precision_score
  from sklearn.model_selection import train_test_split
  from test.test_utils import pairs_learners, ids_pairs_learners
  from metric_learn.sklearn_shims import set_random_state
  from sklearn import clone
  import numpy
  from itertools import product
label_desc: Use image node_id 19 for calling a global function with example usage: test_validate_calibration_params_valid_parameters(estimator, _, valid_args) without return types