code: def test_refit_weights():
      w = np.array([[1.2], [4.4]])
      b = np.array([0.52])
      lr = LogisticRegression(
          epochs=50, eta=0.01, minibatches=1, random_seed=1
      )
  
      lr.fit(X, y)
      w1 = lr.w_[0][0]
      w2 = lr.w_[0][0]
      lr.fit(X, y, init_params=False)
  
      assert w1 != lr.w_[0][0]
      assert w2 != lr.w_[1][0]
      np.testing.assert_almost_equal(lr.w_, w, 2)
      np.testing.assert_almost_equal(lr.b_, b, 2)
  
imports: import sys
  import numpy
  from sklearn.base import clone
  from mlxtend.classifier import LogisticRegression
  from mlxtend.data import iris_data
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 8 for calling a global function with example usage: test_refit_weights() without return types