def METHOD_NAME(
    d_past_feat_dynamic_real: List[int],
    c_past_feat_dynamic_cat: List[int],
    d_feat_dynamic_real: List[int],
    c_feat_dynamic_cat: List[int],
    d_feat_static_real: List[int],
    c_feat_static_cat: List[int],
    quantiles: List[float],
):
    batch_size = 4
    prediction_length = 6
    context_length = 12
    lightning_module = TemporalFusionTransformerLightningModule(
        {
            "context_length": context_length,
            "prediction_length": prediction_length,
            "d_past_feat_dynamic_real": d_past_feat_dynamic_real,
            "c_past_feat_dynamic_cat": c_past_feat_dynamic_cat,
            "d_feat_dynamic_real": d_feat_dynamic_real,
            "c_feat_dynamic_cat": c_feat_dynamic_cat,
            "d_feat_static_real": d_feat_static_real,
            "c_feat_static_cat": c_feat_static_cat,
            "quantiles": quantiles,
        }
    )
    model = lightning_module.model
    feat_static_cat = torch.zeros(
        batch_size, len(c_feat_static_cat), dtype=torch.long
    )
    feat_static_real = torch.ones(batch_size, sum(d_feat_static_real))
    feat_dynamic_cat = torch.zeros(
        batch_size,
        prediction_length + context_length,
        len(c_feat_dynamic_cat),
        dtype=torch.long,
    )
    feat_dynamic_real = torch.ones(
        batch_size,
        prediction_length + context_length,
        sum(d_feat_dynamic_real),
    )
    past_feat_dynamic_cat = torch.zeros(
        batch_size,
        context_length,
        len(c_past_feat_dynamic_cat),
        dtype=torch.long,
    )
    past_feat_dynamic_real = torch.ones(
        batch_size, context_length, sum(d_past_feat_dynamic_real)
    )
    past_target = torch.ones(batch_size, context_length)
    past_observed_values = torch.ones(batch_size, context_length)
    future_target = torch.ones(batch_size, prediction_length)
    future_observed_values = torch.ones(batch_size, prediction_length)
    (
        past_covariates,
        future_covariates,
        static_covariates,
        loc,
        scale,
    ) = model._preprocess(
        past_target=past_target,
        past_observed_values=past_observed_values,
        feat_static_real=feat_static_real,
        feat_static_cat=feat_static_cat,
        feat_dynamic_real=feat_dynamic_real,
        feat_dynamic_cat=feat_dynamic_cat,
        past_feat_dynamic_real=past_feat_dynamic_real,
        past_feat_dynamic_cat=past_feat_dynamic_cat,
    )
    for x in past_covariates:
        assert x.shape == (batch_size, context_length, model.d_var)
    for x in future_covariates:
        assert x.shape == (batch_size, prediction_length, model.d_var)
    for x in static_covariates:
        assert x.shape == (batch_size, model.d_var)
    assert loc.shape == scale.shape == (batch_size, 1)
    output = model(
        past_target=past_target,
        past_observed_values=past_observed_values,
        feat_static_real=feat_static_real,
        feat_static_cat=feat_static_cat,
        feat_dynamic_real=feat_dynamic_real,
        feat_dynamic_cat=feat_dynamic_cat,
        past_feat_dynamic_real=past_feat_dynamic_real,
        past_feat_dynamic_cat=past_feat_dynamic_cat,
    )
    assert output.shape == (batch_size, len(quantiles), prediction_length)
    batch = dict(
        past_target=past_target,
        past_observed_values=past_observed_values,
        past_feat_dynamic_real=past_feat_dynamic_real,
        past_feat_dynamic_cat=past_feat_dynamic_cat,
        feat_dynamic_real=feat_dynamic_real,
        feat_dynamic_cat=feat_dynamic_cat,
        feat_static_real=feat_static_real,
        feat_static_cat=feat_static_cat,
        future_target=future_target,
        future_observed_values=future_observed_values,
    )
    assert lightning_module.training_step(batch, batch_idx=0).shape == ()
    assert lightning_module.validation_step(batch, batch_idx=0).shape == ()