code: def test_pair_distance_dim(estimator, build_dataset):
      # scoring of 3D arrays should return 1D array (several tuples),
      # and scoring of 2D arrays (one tuple) should return an error (like
      # scikit-learn's error when scoring 1D arrays)
      input_data, labels, _, X = build_dataset()
      model = clone(estimator)
      set_random_state(model)
      model.fit(*remove_y(estimator, input_data, labels))
      tuples = np.array(list(product(X, X)))
      assert model.pair_distance(tuples).shape == (tuples.shape[0],)
      context = make_context(estimator)
      msg = (
          "3D array of formed tuples expected{}. Found 2D array "
          "instead:\ninput={}. Reshape your data and/or use a preprocessor.\n".format(
              context, tuples[1]
          )
      )
      with pytest.raises(ValueError) as raised_error:
          model.pair_distance(tuples[1])
      assert str(raised_error.value) == msg
  
imports: from itertools import product
  import pytest
  import numpy
  from numpy.linalg import LinAlgError
  from numpy.testing import assert_array_almost_equal, assert_allclose, assert_array_equal
  from scipy.spatial.distance import pdist, squareform, mahalanobis
  from scipy.stats import ortho_group
  from sklearn import clone
  from sklearn.cluster import DBSCAN
  from sklearn.datasets import make_spd_matrix, make_blobs
  from sklearn.utils import check_random_state, shuffle
  from sklearn.utils.multiclass import type_of_target
  from metric_learn.sklearn_shims import set_random_state
  from metric_learn._util import make_context, _initialize_metric_mahalanobis
  from metric_learn.sdml import _BaseSDML
  from metric_learn.base_metric import _QuadrupletsClassifierMixin, _TripletsClassifierMixin, _PairsClassifierMixin
  from metric_learn.exceptions import NonPSDError
  from test.test_utils import ids_metric_learners, metric_learners, remove_y, ids_classifiers
label_desc: Use image node_id 5 for calling a global function with example usage: test_pair_distance_dim(estimator, build_dataset) without return types