force_first_round_loss=True for SNPE-A

sbi/inference/snpe/snpe_a.py

@@ -106,7 +106,6 @@ def train(
         clip_max_norm: Optional[float] = 5.0,
         calibration_kernel: Optional[Callable] = None,
         resume_training: bool = False,
-        force_first_round_loss: bool = False,
         retrain_from_scratch: bool = False,
         show_train_summary: bool = False,
         dataloader_kwargs: Optional[Dict] = None,
@@ -144,8 +143,6 @@ def train(
             force_first_round_loss: If `True`, train with maximum likelihood,
                 i.e., potentially ignoring the correction for using a proposal
                 distribution different from the prior.
-            force_first_round_loss: If `True`, train with maximum likelihood,
-                regardless of the proposal distribution.
             retrain_from_scratch: Whether to retrain the conditional density
                 estimator for the posterior from scratch each round. Not supported for
                 SNPE-A.
@@ -174,6 +171,7 @@ def train(
 
         # SNPE-A always discards the prior samples.
         kwargs["discard_prior_samples"] = True
+        kwargs["force_first_round_loss"] = True
 
         self._round = max(self._data_round_index)