How to use the smdebug.xgboost.Hook function in smdebug

def create_tornasole_hook(out_dir, train_data=None, validation_data=None, frequency=1):

    save_config = SaveConfig(save_interval=frequency)
    hook = Hook(
        out_dir=out_dir,
        save_config=save_config,
        train_data=train_data,
        validation_data=validation_data,
    )

    return hook

def test_hook(tmpdir):
    save_config = SaveConfig(save_steps=[0, 1, 2, 3])
    out_dir = os.path.join(tmpdir, str(uuid.uuid4()))
    hook = Hook(out_dir=out_dir, save_config=save_config)
    assert has_training_ended(out_dir) is False
    run_xgboost_model(hook=hook)

def test_hook_save_every_step(tmpdir):
    save_config = SaveConfig(save_interval=1)
    out_dir = os.path.join(tmpdir, str(uuid.uuid4()))
    hook = Hook(out_dir=out_dir, save_config=save_config)
    run_xgboost_model(hook=hook)
    trial = create_trial(out_dir)
    assert trial.steps() == list(range(10))

def test_hook_shap(tmpdir):
    np.random.seed(42)
    train_data = np.random.rand(10, 10)
    train_label = np.random.randint(2, size=10)
    dtrain = xgboost.DMatrix(train_data, label=train_label)

    out_dir = os.path.join(tmpdir, str(uuid.uuid4()))
    hook = Hook(
        out_dir=out_dir, include_collections=["average_shap", "full_shap"], train_data=dtrain
    )
    run_xgboost_model(hook=hook)

    trial = create_trial(out_dir)
    tensors = trial.tensor_names()
    assert len(tensors) > 0
    assert "average_shap" in trial.collections()
    assert "full_shap" in trial.collections()
    assert any(t.startswith("average_shap/") for t in tensors)
    assert any(t.startswith("full_shap/") for t in tensors)
    assert not any(t.endswith("/bias") for t in tensors)
    average_shap_tensors = [t for t in tensors if t.startswith("average_shap/")]
    average_shap_tensor_name = average_shap_tensors.pop()
    assert trial.tensor(average_shap_tensor_name).value(0).shape == (1,)
    full_shap_tensors = [t for t in tensors if t.startswith("full_shap/")]

def helper_xgboost_tests(collection, save_config):
    coll_name, coll_regex = collection

    run_id = "trial_" + coll_name + "-" + datetime.now().strftime("%Y%m%d-%H%M%S%f")
    trial_dir = os.path.join(SMDEBUG_XG_HOOK_TESTS_DIR, run_id)

    hook = XG_Hook(
        out_dir=trial_dir,
        include_collections=[coll_name],
        save_config=save_config,
        export_tensorboard=True,
    )

    simple_xg_model(hook)
    hook.close()

    saved_scalars = ["scalar/xg_num_steps", "scalar/xg_before_train", "scalar/xg_after_train"]
    verify_files(trial_dir, save_config, saved_scalars)

def test_hook_validation(tmpdir):
    np.random.seed(42)
    train_data = np.random.rand(5, 10)
    train_label = np.random.randint(2, size=5)
    dtrain = xgboost.DMatrix(train_data, label=train_label)
    valid_data = np.random.rand(5, 10)
    valid_label = np.random.randint(2, size=5)
    dvalid = xgboost.DMatrix(valid_data, label=valid_label)

    out_dir = os.path.join(tmpdir, str(uuid.uuid4()))
    hook = Hook(
        out_dir=out_dir,
        include_collections=["labels", "predictions"],
        train_data=dtrain,
        validation_data=dvalid,
    )
    run_xgboost_model(hook=hook)

    trial = create_trial(out_dir)
    tensors = trial.tensor_names()
    assert len(tensors) > 0
    assert "labels" in trial.collections()
    assert "predictions" in trial.collections()
    assert "labels" in tensors
    assert "predictions" in tensors

def create_hook(out_dir, train_data=None, validation_data=None, frequency=1):

    save_config = SaveConfig(save_interval=frequency)
    hook = Hook(
        out_dir=out_dir,
        save_config=save_config,
        train_data=train_data,
        validation_data=validation_data,
    )

    return hook

def create_hook(out_dir, train_data=None, validation_data=None, frequency=1):

    save_config = SaveConfig(save_interval=frequency)
    hook = Hook(
        out_dir=out_dir,
        save_config=save_config,
        train_data=train_data,
        validation_data=validation_data,
    )

    return hook