Clean up sample_prior_predictive

Since model_builder must create model object and set data, data only
needs to be set if model object already exists and no need to check for
model object before sampling.

pymc_experimental/model_builder.py

@@ -601,17 +601,16 @@ def sample_prior_predictive(
 
         if self.model is None:
             self.build_model(X_pred, y_pred)
-
-        self._data_setter(X_pred, y_pred)
-        if self.model is not None:
-            with self.model:  # sample with new input data
-                prior_pred: az.InferenceData = pm.sample_prior_predictive(samples, **kwargs)
-                self.set_idata_attrs(prior_pred)
-                if extend_idata:
-                    if self.idata is not None:
-                        self.idata.extend(prior_pred)
-                    else:
-                        self.idata = prior_pred
+        else:
+            self._data_setter(X_pred, y_pred)
+        with self.model:  # sample with new input data
+            prior_pred: az.InferenceData = pm.sample_prior_predictive(samples, **kwargs)
+            self.set_idata_attrs(prior_pred)
+            if extend_idata:
+                if self.idata is not None:
+                    self.idata.extend(prior_pred)
+                else:
+                    self.idata = prior_pred
 
         prior_predictive_samples = az.extract(prior_pred, "prior_predictive", combined=combined)