code: def test_sgd_optimizer_no_momentum():
      params = [np.zeros(shape) for shape in shapes]
      rng = np.random.RandomState(0)
  
      for lr in [10**i for i in range(-3, 4)]:
          optimizer = SGDOptimizer(
              params, lr, momentum=0, nesterov=False
          )
          grads = [rng.random_sample(shape) for shape in shapes]
          expected = [
              param - lr * grad for param, grad in zip(params, grads)
          ]
          optimizer.update_params(params, grads)
  
          for exp, param in zip(expected, params):
              assert_array_equal(exp, param)
  
file_path: scikit-learn/sklearn/neural_network/tests/test_stochastic_optimizers.py
imports: import numpy
  from sklearn.neural_network._stochastic_optimizers import AdamOptimizer, BaseOptimizer, SGDOptimizer
  from sklearn.utils._testing import assert_array_equal
label_desc: Use image node_id 2 for calling a global function with example usage: test_sgd_optimizer_no_momentum() without return types