code: def test_raise_big_number_of_features():
      triplets, _, _, X = build_triplets(with_preprocessor=False)
      triplets = triplets[:3, :, :]
      estimator = SCML(n_basis=320)
      set_random_state(estimator)
      with pytest.raises(ValueError) as exc_info:
          estimator.fit(triplets)
      assert (
          exc_info.value.args[0]
          == "Number of features (4) is greater than the number of triplets(3)."
          "\nConsider using dimensionality reduction or using another basis "
          "generation scheme."
      )
  
file_path: metric-learn/test/test_triplets_classifiers.py
imports: import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.model_selection import train_test_split
  from metric_learn import SCML
  from test.test_utils import triplets_learners, ids_triplets_learners, build_triplets
  from metric_learn.sklearn_shims import set_random_state
  from sklearn import clone
  import numpy
  from numpy.testing import assert_array_equal
label_desc: Use image node_id 5 for calling a global function with example usage: test_raise_big_number_of_features() without return types