def METHOD_NAME(shap_values: torch.tensor,) -> List[go.Figure]:
    average_shap_value_over_batches = shap_values.mean(axis="batches")
    x = [i for i in range(shap_values.align_to("observations", ...).shape[0])]
    y = [i for i in range(shap_values.align_to("preds", ...).shape[0])]
    figs: List[go.Figure] = []
    for shap_values_features in average_shap_value_over_batches.align_to(
        "features", ...
    ):
        fig = go.Figure()
        heatmap = go.Heatmap(
            z=shap_values_features,
            x=x,
            y=y,
            colorbar=dict(title=dict(side="right", text="feature values")),
            colorscale=px.colors.sequential.Bluered,
        )
        fig.add_trace(heatmap)
        fig.update_xaxes(title_text="sequence history steps")
        fig.update_yaxes(title_text="prediction steps")
        figs.append(fig)
    return figs