class_docstr: A simple pairs classifier for testing purposes, that will just have
  identity as components_, and a string threshold so that it returns an
  error if not explicitely set.
code: def fit(self, pairs, y):
      pairs, y = self._prepare_inputs(pairs, y, type_of_inputs="tuples")
      self.components_ = np.atleast_2d(np.identity(pairs.shape[2]))
      # self.threshold_ is not set.
      return self
  
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 1 for calling the IdentityPairsClassifier obj's underlying member method code with example usage: obj.fit(pairs, y) and returns: self