code: def prior_to_xarray(self):
          
          # filter prior_predictive
          prior_predictive = self.prior_predictive
          columns = self.prior[0].columns
          if prior_predictive is None or (
              isinstance(prior_predictive, str) and prior_predictive.lower().endswith(".csv")
          ):
              prior_predictive = []
          elif isinstance(prior_predictive, str):
              prior_predictive = [col for col in columns if prior_predictive == col.split(".")[0]]
          else:
              prior_predictive = [
                  col
                  for col in columns
                  if any(item == col.split(".")[0] for item in prior_predictive)
              ]
  
          invalid_cols = prior_predictive
          valid_cols = [col for col in columns if col not in invalid_cols]
          data = _unpack_dataframes([item[valid_cols] for item in self.prior])
          return dict_to_dataset(data, coords=self.coords, dims=self.dims)
docs: Convert prior samples to xarray.