fix: optimizer was not used in workflow with multiple fits

For the optimizer to be used, the approximator.compile function has to
be called. This was not the case. I adapted the `setup_optimizer`
function to match the description in its docstring, and made the
compilation conditional on its output. The output indicates if a new
optimizer was configured.

Author: vpratz

bayesflow/workflows/basic_workflow.py

@@ -914,6 +914,7 @@ def build_optimizer(self, epochs: int, num_batches: int, strategy: str) -> keras
             self.optimizer = keras.optimizers.Adam(learning_rate, clipnorm=1.5)
         else:
             self.optimizer = keras.optimizers.AdamW(learning_rate, weight_decay=5e-3, clipnorm=1.5)
+        return self.optimizer
 
     def _fit(
         self,
@@ -955,9 +956,10 @@ def _fit(
             else:
                 kwargs["callbacks"] = [model_checkpoint_callback]
 
-        self.build_optimizer(epochs, dataset.num_batches, strategy=strategy)
-
-        if not self.approximator.built:
+        # returns None if no new optimizer was built and assigned to self.optimizer, which indicates we do not have
+        # to (re)compile the approximator.
+        optimizer = self.build_optimizer(epochs, dataset.num_batches, strategy=strategy)
+        if optimizer is not None:
             self.approximator.compile(optimizer=self.optimizer, metrics=kwargs.pop("metrics", None))
 
         try: