def input_for_dynamic_balance_class_sampler() -> List[Tuple[list, float]]:
    """
    This function generates some valid inputs for DynamicBalanceClassSampler

    Returns:
        inputs in the folowing order: (labels, exp_l)
    """
    labels = [
        # class imbalance
        np.array([0] * 100 + [1] * 10 + [2] * 20),
        # uniform class distribution
        np.array([0] * 10 + [1] * 10 + [2] * 10),
        # random class distribution
        np.random.randint(0, 4, size=(200,)),
    ]
    exp_lambda = np.linspace(0.1, 0.95, 11)
    input_cases = np.transpose(
        [np.tile(labels, len(exp_lambda)), np.repeat(exp_lambda, len(labels))]
    )
    return input_cases

    This function generates some valid inputs for DynamicBalanceClassSampler

    Returns:
        inputs in the folowing order: (labels, exp_l)
    
tests/catalyst/data/test_sampler.py