Add constraints

src/zfitwrapper/BaseFitter.py

 from abc import ABC, abstractmethod
 import numpy as np
-from typing import Any, Dict, Mapping, Optional, Type, Union, List, TypeVar
+from typing import Any, Dict, Mapping, Optional, Type, Union, List, TypeVar, Tuple
 import zfit
 import zfit.core
 from hepstats.hypotests import Discovery
@@ -25,7 +25,7 @@ class BaseFitter(ABC):
         global_optimizer: str = "",
         global_optimizer_args: Dict[Any, Any] = {},
         truncated_limits: Optional[
-            tuple[float, float] | list[tuple[float, float]]
+            Union[Tuple[float, float], List[Tuple[float, float]]]
         ] = None,
     ) -> None:
         self.model = model
@@ -140,7 +140,11 @@ class BaseFitter(ABC):
 
     def _single_fit(self) -> None:
         assert isinstance(self.eval_model, zfit.core.interfaces.ZfitPDF)
-        nll = self.lossfunction(model=self.eval_model, data=self.data)
+        nll = self.lossfunction(
+            model=self.eval_model,
+            data=self.data,
+            constraints=self.model.constraints,
+        )
         self.nll = nll
         if self.global_optimizer:
             minimizer_global = getattr(zfit.minimize, self.global_optimizer)(
@@ -205,7 +209,7 @@ class BaseFitter(ABC):
         self,
         calculator: str = "AsymptoticCalculator",
         calculator_kwargs: Dict[str, Any] = {"asimov_bins": 100},
-    ) -> AsymptoticCalculator | FrequentistCalculator:
+    ) -> Union[AsymptoticCalculator, FrequentistCalculator]:
         assert self.nll is not None, "No NLL yet calculated, please check the fit"
         if calculator == "AsymptoticCalculator":
             return AsymptoticCalculator(self.nll, self.minimizer, **calculator_kwargs)
@@ -220,7 +224,7 @@ class BaseFitter(ABC):
         parameter_range: np.ndarray,
         calculator: str = "AsymptoticCalculator",
         calculator_kwargs: Dict[str, Any] = {"asimov_bins": 100},
-    ) -> dict[str, float]:
+    ) -> Dict[str, float]:
         parameter = self.model.get_modelparameter(parameter_name)
         calculator = self.get_calculator(
             calculator=calculator, calculator_kwargs=calculator_kwargs
@@ -241,7 +245,7 @@ class BaseFitter(ABC):
         alpha: float = 0.05,
         calculator: str = "AsymptoticCalculator",
         calculator_kwargs: Dict[str, Any] = {"asimov_bins": 100},
-    ) -> dict[str, float]:
+    ) -> Dict[str, float]:
         parameter = self.model.get_modelparameter(parameter_name)
         calculator = self.get_calculator(
             calculator=calculator, calculator_kwargs=calculator_kwargs