code: def test_logistic_regression_gd():
      w = np.array([[1.2], [4.4]])
      b = np.array([0.52])
      lr = LogisticRegression(
          epochs=100, eta=0.01, minibatches=1, random_seed=1
      )
  
      lr.fit(X, y)
      np.testing.assert_almost_equal(lr.w_, w, 2)
      np.testing.assert_almost_equal(lr.b_, b, 2)
      y_pred = lr.predict(X)
      acc = np.sum(y == y_pred, axis=0) / float(X.shape[0])
      assert acc == 1.0, "Acc: %s" % acc
  
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 3 for calling a global function with example usage: test_logistic_regression_gd() without return types