Commented out graphviz visualization fucntion

skpro/regression/bayesian.py

@@ -9,7 +9,8 @@
 __author__ = ["meraldoantonio"]
 
 from skpro.regression.base import BaseProbaRegressor
-from skpro.utils.validation._dependencies import _check_soft_dependencies
+
+# from skpro.utils.validation._dependencies import _check_soft_dependencies
 
 
 class BayesianLinearRegressor(BaseProbaRegressor):
@@ -178,16 +179,16 @@ def _fit(self, X, y):
             self.idata.add_groups(training_data=training_data.to_xarray())
         return self
 
-    def visualize_model(self, **kwargs):
-        """Use Graphviz to visualize the model flow."""
-        _check_soft_dependencies(
-            "graphviz", msg="You need to install Graphviz to use this method!"
-        )
-        import pymc as pm
+    # def visualize_model(self, **kwargs):
+    #     """Use Graphviz to visualize the model flow."""
+    #     _check_soft_dependencies(
+    #         "graphviz", msg="You need to install Graphviz to use this method!"
+    #     )
+    #     import pymc as pm
 
-        assert self._is_fitted, "Model must be fitted before visualization can be done!"
+    #     assert self._is_fitted, "You need to fit the model before visualizing it!"
 
-        return pm.model_to_graphviz(self.model, **kwargs)
+    #     return pm.model_to_graphviz(self.model, **kwargs)
 
     def _sample_dataset(self, group_name, return_type=None):
         """