code: def test_labels():
      X = np.array([[1, 2], [3, 4]])
      y = np.array([-1, 1])
      lr = SoftmaxRegression(
          epochs=200, eta=0.005, minibatches=1, random_seed=1
      )
      assert_raises(
          AttributeError,
          "y array must not contain negative labels.\nFound [-1  1]",
          lr.fit,
          X,
          y,
      )
  
file_path: mlxtend/mlxtend/classifier/tests/test_softmax_regression.py
imports: import numpy
  from sklearn.base import clone
  from mlxtend.classifier import SoftmaxRegression
  from mlxtend.data import iris_data
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 1 for calling a global function with example usage: test_labels() without return types