code: def test_multi_logistic_probas():
      lr = SoftmaxRegression(
          epochs=200, eta=0.005, minibatches=1, random_seed=1
      )
      lr.fit(X, y)
      idx = [0, 50, 149]  # sample labels: 0, 1, 2
      y_pred = lr.predict_proba(X[idx])
      exp = np.array(
          [[0.99, 0.01, 0.00], [0.01, 0.88, 0.11], [0.00, 0.02, 0.98]]
      )
      np.testing.assert_almost_equal(y_pred, exp, 2)
  
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 10 for calling a global function with example usage: test_multi_logistic_probas() without return types