code: def test_retrain():
      mlp = MLP(
          epochs=5,
          eta=0.05,
          hidden_layers=[10],
          minibatches=len(y),
          random_seed=1,
      )
  
      mlp.fit(X, y)
      cost_1 = mlp.cost_[-1]
      mlp.fit(X, y)
      cost_2 = mlp.cost_[-1]
      mlp.fit(X, y, init_params=False)
      cost_3 = mlp.cost_[-1]
  
      assert cost_2 == cost_1
      assert cost_3 < (cost_2 / 2.0)
  
file_path: mlxtend/mlxtend/classifier/tests/test_multilayerperceptron.py
imports: import numpy
  from sklearn.base import clone
  from mlxtend.classifier import MultiLayerPerceptron
  from mlxtend.data import iris_data
  from mlxtend.utils import assert_raises
label_desc: Use image node_id 13 for calling a global function with example usage: test_retrain() without return types