fix: gridsearch parameters check

darts/models/forecasting/forecasting_model.py

@@ -1775,12 +1775,33 @@ def gridsearch(
                 )
             )
 
+        if "model" in parameters:
+            valid_model_list = isinstance(parameters["model"], list)
+            valid_nested_params = parameters["model"].get(
+                "wrapped_model_class"
+            ) and all(
+                isinstance(params, (list, np.ndarray))
+                for p_name, params in parameters["model"].items()
+                if p_name != "wrapped_model_class"
+            )
+            if not (valid_model_list or valid_nested_params):
+                raise_log(
+                    ValueError(
+                        "The 'model' entry in `parameters` must either be a list of instantiated models or "
+                        "a dictionary containing as keys hyperparameter names, and as values lists of values "
+                        "plus a 'wrapped_model_class': model_cls item.",
+                        logger,
+                    )
+                )
+
         if not all(
-            isinstance(params, (list, np.ndarray)) for params in parameters.values()
+            isinstance(params, (list, np.ndarray))
+            for p_name, params in parameters.items()
+            if p_name != "model"
         ):
             raise_log(
                 ValueError(
-                    "Every value in the `parameters` dictionary should be a list or a np.ndarray."
+                    "Every hyper-parameter value in the `parameters` dictionary should be a list or a np.ndarray."
                 ),
                 logger,
             )
@@ -1812,19 +1833,12 @@ def gridsearch(
 
         if "model" in parameters:
             # Ask if model has been passed as a dictionary. This implies that the arguments
-            # of the wrapped model must be passed to the grid. If 'model' is passed as a
-            # list of instances of scikit-learn models, the behavior should work like
-            # any argument passed to the Darts model."
-            if isinstance(parameters["model"], dict):
-                if "model_class" not in parameters["model"]:
-                    raise_log(
-                        ValueError(
-                            "When the 'model' key is set as a dictionary, it must contain "
-                            "the 'model_class' key, which represents the class of the model "
-                            "to be wrapped."
-                        )
-                    )
-                wrapped_model_class = parameters["model"].pop("model_class")
+            # of the wrapped model must be passed to the grid.
+            if (
+                isinstance(parameters["model"], dict)
+                and "wrapped_model_class" in parameters["model"]
+            ):
+                wrapped_model_class = parameters["model"].pop("wrapped_model_class")
                 # Create a flat dictionary by adding a suffix to the arguments of the wrapped model in
                 # order to distinguish them from the other arguments of the Darts model
                 parameters.update({