code: def test_refit_weights():
      t = np.array([[0.13, -0.12], [-3.07, 3.05]])
      lr = SoftmaxRegression(
          epochs=100, eta=0.005, minibatches=1, random_seed=1
      )
  
      lr.fit(X_bin, y_bin)
      w1 = lr.w_[0][0]
      w2 = lr.w_[0][0]
      lr.fit(X_bin, y_bin, init_params=False)
  
      assert w1 != lr.w_[0][0]
      assert w2 != lr.w_[1][0]
      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 3 for calling a global function with example usage: test_refit_weights() without return types