code: def __init__(
      self,
      sparsity_param=0.01,
      n_constraints=None,
      verbose=False,
      preprocessor=None,
      random_state=None,
  ):
      # this init makes SDML stable for scikit-learn examples.
      super(Stable_SDML_Supervised, self).__init__(
          sparsity_param=sparsity_param,
          n_constraints=n_constraints,
          verbose=verbose,
          preprocessor=preprocessor,
          balance_param=1e-5,
          prior="identity",
          random_state=random_state,
      )
  
imports: import pytest
  import unittest
  from sklearn.utils.estimator_checks import check_estimator
  from sklearn.base import TransformerMixin
  from sklearn.pipeline import make_pipeline
  from sklearn.utils import check_random_state
  from metric_learn.sklearn_shims import assert_allclose_dense_sparse, set_random_state, _get_args, is_public_parameter, get_scorer
  from metric_learn import Covariance, LFDA, LMNN, MLKR, NCA, ITML_Supervised, LSML_Supervised, MMC_Supervised, RCA_Supervised, SDML_Supervised, SCML_Supervised
  from sklearn import clone
  import numpy
  from sklearn.model_selection import cross_val_score, cross_val_predict, train_test_split, KFold
  from test.test_utils import metric_learners, ids_metric_learners, mock_preprocessor, tuples_learners, ids_tuples_learners, pairs_learners, ids_pairs_learners, remove_y, metric_learners_pipeline, ids_metric_learners_pipeline
label_desc: Use image node_id 1 to create a new Stable_SDML_Supervised object from inherited base classes: SDML_Supervised with example: obj = Stable_SDML_Supervised(sparsity_param, n_constraints, verbose, preprocessor, random_state)