code: def test_label_encoder_missing_values_numeric(setup):
      values = np.array([3, 1, np.nan, 5, 3, np.nan], dtype=float)
      values_t = mt.tensor(values)
      le = LabelEncoder()
      assert_array_equal(
          le.fit_transform(values_t).fetch(), [1, 0, 3, 2, 1, 3]
      )
  
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 12 for calling a global function with example usage: test_label_encoder_missing_values_numeric(setup) without return types