def METHOD_NAME(self):
    obs = APoTObserver(b=6, k=2)
    obs.min_val = torch.tensor([0.0])
    obs.max_val = torch.tensor([1.0])
    alpha, gamma, quantization_levels, level_indices = obs.calculate_qparams(signed=False)
    alpha_test = torch.max(-obs.min_val, obs.max_val)
    # check alpha value
    self.assertEqual(alpha, alpha_test)
    # calculate expected gamma value
    gamma_test = 0
    for i in range(3):
        gamma_test += 2**(-i)
    gamma_test = 1 / gamma_test
    # check gamma value
    self.assertEqual(gamma, gamma_test)
    # check quantization levels size
    quantlevels_size_test = int(len(quantization_levels))
    quantlevels_size = 2**6
    self.assertEqual(quantlevels_size_test, quantlevels_size)
    # check level indices size
    levelindices_size_test = int(len(level_indices))
    self.assertEqual(levelindices_size_test, 64)
    # check level indices unique values
    level_indices_test_list = level_indices.tolist()
    self.assertEqual(len(level_indices_test_list), len(set(level_indices_test_list)))