def METHOD_NAME(self, lab, seed):
    num_classes = 10
    means = [
        np.array([np.random.uniform(high=10), np.random.uniform(high=10)])
        for _ in range(num_classes)
    ]
    sigmas = [np.random.uniform(high=1) for _ in range(num_classes)]
    class_stats = list(zip(means, sigmas))
    num_samples = 2000
    def generate_data_iid():
        # This should be IID, resulting in a larger p-value
        samples = []
        labels = []
        for _ in range(num_samples):
            label = np.random.choice(num_classes)
            mean, sigma = class_stats[label]
            sample = np.random.normal(mean, sigma)
            samples.append(sample)
            labels.append(label)
        samples = np.array(samples)
        labels = np.array(labels)
        dataset = {"features": samples, "labels": labels}
        return dataset
    dataset = generate_data_iid()
    embeddings = dataset["features"]
    # Create new issue manager, ignore the lab assigned for this test
    if seed == "default":
        issue_manager = NonIIDIssueManager(
            datalab=lab,
            metric="euclidean",
            k=10,
        )
    else:
        issue_manager = NonIIDIssueManager(
            datalab=lab,
            metric="euclidean",
            k=10,
            seed=seed,
        )
    issue_manager.find_issues(features=embeddings)
    p_value = issue_manager.info["p-value"]
    # Run again with the same seed
    issue_manager.find_issues(features=embeddings)
    p_value2 = issue_manager.info["p-value"]
    assert p_value > 0.0
    if seed is not None or seed == "default":
        assert p_value == p_value2
    else:
        assert p_value != p_value2