Update comment

target_permutation_importances/functional.py

@@ -345,7 +345,7 @@ def compute(
     Args:
         model_cls: The constructor/class of the model.
         model_cls_params: The parameters to pass to the model constructor.
-        model_fit_params: A function that return parameters to pass to the model fit method.
+        model_fit_params: A Dict or A function that return parameters to pass to the model fit method.
         X: The input data.
         y: The target vector.
         num_actual_runs: Number of actual runs. Defaults to 2.
@@ -379,7 +379,7 @@ def compute(
             model_cls_params={ # The parameters to pass to the model constructor. Update this based on your needs.
                 "n_jobs": -1,
             },
-            model_fit_params=lambda _: {}, # The parameters to pass to the model fit method. Update this based on your needs.
+            model_fit_params={}, # The parameters to pass to the model fit method. Update this based on your needs.
             X=Xpd, # pd.DataFrame, np.ndarray
             y=data.target, # pd.Series, np.ndarray
             num_actual_runs=2,
@@ -424,9 +424,10 @@ def _model_builder(is_random_run: bool, run_idx: int) -> Any:
         return model_cls(**_model_cls_params)
 
     def _model_fitter(model: Any, X: XType, y: YType) -> Any:
-        if isinstance(model_fit_params, dict):
+        if isinstance(model_fit_params, dict):  # pragma: no cover
             _model_fit_params = model_fit_params.copy()
         else:
+            # Assume it is a function
             _model_fit_params = model_fit_params(
                 list(X.columns) if isinstance(X, pd.DataFrame) else None,
             )