code: def test_label_encoder_empty_array(setup, values):
      values_t = mt.tensor(values)
  
      le = LabelEncoder()
      le.fit(values_t)
      # test empty transform
      transformed = le.transform(mt.array([]))
      assert_array_equal(np.array([]), transformed)
      # test empty inverse transform
      inverse_transformed = le.inverse_transform([])
      assert_array_equal(np.array([]), inverse_transformed)
  
imports: import numpy
  import pytest
  import scipy.sparse
  from sklearn.preprocessing._label import _inverse_binarize_thresholding
  from sklearn.preprocessing._label import _inverse_binarize_multiclass
  from sklearn.utils._testing import assert_array_equal, ignore_warnings
  from sklearn.utils.multiclass import type_of_target
  from ....None import tensor
  from ..None import LabelBinarizer, label_binarize, LabelEncoder
label_desc: Use image node_id 16 for calling a global function with example usage: test_label_encoder_empty_array(setup, values) without return types