code: def test_multi_logistic_regression_gd_weights():
      t = np.array([[-0.95, -2.45, 3.4], [-3.95, 2.34, 1.59]])
      lr = SoftmaxRegression(
          epochs=200, eta=0.005, minibatches=1, random_seed=1
      )
      lr.fit(X, y)
      np.testing.assert_almost_equal(lr.w_, t, 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 9 for calling a global function with example usage: test_multi_logistic_regression_gd_weights() without return types