Conformal prediction intervals insample data nixtla

Given the documentation of nixtla y dont find any way to compute the prediction intervals for insample prediction (training data) but just for future predicitons.

I put an example of what I can achieve but just to predict (future).

from statsforecast.models import SeasonalExponentialSmoothing, ADIDA, ARIMA
from statsforecast.utils import ConformalIntervals

# Create a list of models and instantiation parameters 
intervals = ConformalIntervals(h=24, n_windows=2)

models = [
    SeasonalExponentialSmoothing(season_length=24,alpha=0.1, prediction_intervals=intervals),
    ADIDA(prediction_intervals=intervals),
    ARIMA(order=(24,0,12), season_length=24, prediction_intervals=intervals),
]

sf = StatsForecast(
    df=train, 
    models=models, 
    freq='H', 
)

levels = [80, 90] # confidence levels of the prediction intervals 

forecasts = sf.forecast(h=24, level=levels)
forecasts = forecasts.reset_index()
forecasts.head()

So my goal will be to do something like:

 forecasts = sf.forecast(df_x, level=levels)

So we can have any prediction intervals in the training set.

from Conformal prediction intervals insample data nixtla