def METHOD_NAME(self):
    obs = APoTObserver(b=4, 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=True)
    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(2):
        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))
    self.assertEqual(quantlevels_size_test, 49)
    # check negatives of each element contained
    # in quantization levels
    quantlevels_test_list = quantization_levels.tolist()
    negatives_contained = True
    for ele in quantlevels_test_list:
        if -ele not in quantlevels_test_list:
            negatives_contained = False
    self.assertTrue(negatives_contained)
    # check level indices size
    levelindices_size_test = int(len(level_indices))
    self.assertEqual(levelindices_size_test, 49)
    # check level indices unique elements
    level_indices_test_list = level_indices.tolist()
    self.assertEqual(len(level_indices_test_list), len(set(level_indices_test_list)))