code: def test_like_dist_belief_frobenius_cg01():
      db_grad, db_out = _db_params_frobenius_cg01()
      num_samples = len(db_grad)
      var0 = tf.Variable([0.0] * num_samples)
      grads0 = tf.constant([0.0] * num_samples)
      ord = "fro"
      cg_opt = cg_lib.ConditionalGradient(
          learning_rate=0.1, lambda_=0.1, ord=ord
      )
  
      for i in range(num_samples):
          grads0 = tf.constant(db_grad[i])
          cg_opt.apply_gradients(zip([grads0], [var0]))
          np.testing.assert_allclose(
              np.array(db_out[i]), var0.numpy(), rtol=1e-06, atol=1e-06
          )
  
imports: import numpy
  import pytest
  import platform
  import tensorflow
  from tensorflow_addons.utils import test_utils
  from tensorflow_addons.optimizers import conditional_gradient
label_desc: Use image node_id 15 for calling a global function with example usage: test_like_dist_belief_frobenius_cg01() without return types