diff --git a/prototorch/models/abstract.py b/prototorch/models/abstract.py
index b90ca88..7642228 100644
--- a/prototorch/models/abstract.py
+++ b/prototorch/models/abstract.py
@@ -43,7 +43,7 @@ class ProtoTorchBolt(pl.LightningModule):
             return optimizer
 
     def reconfigure_optimizers(self):
-        self.trainer.accelerator.setup_optimizers(self.trainer)
+        self.trainer.strategy.setup_optimizers(self.trainer)
 
     def __repr__(self):
         surep = super().__repr__()
diff --git a/prototorch/models/callbacks.py b/prototorch/models/callbacks.py
index f12d162..2489cb4 100644
--- a/prototorch/models/callbacks.py
+++ b/prototorch/models/callbacks.py
@@ -137,4 +137,4 @@ class GNGCallback(pl.Callback):
             pl_module.errors[
                 worst_neighbor] = errors[worst_neighbor] * self.reduction
 
-            trainer.accelerator.setup_optimizers(trainer)
+            trainer.strategy.setup_optimizers(trainer)
diff --git a/prototorch/models/glvq.py b/prototorch/models/glvq.py
index c5556ca..089ae30 100644
--- a/prototorch/models/glvq.py
+++ b/prototorch/models/glvq.py
@@ -148,7 +148,7 @@ class SiameseGLVQ(GLVQ):
         x, protos = [arr.view(arr.size(0), -1) for arr in (x, protos)]
         latent_x = self.backbone(x)
 
-        bb_grad = self.backbone._weights.requires_grad
+        bb_grad = any([el.requires_grad for el in self.backbone.parameters()])
 
         self.backbone.requires_grad_(bb_grad and self.both_path_gradients)
         latent_protos = self.backbone(protos)
diff --git a/prototorch/models/unsupervised.py b/prototorch/models/unsupervised.py
index f59157d..64a324a 100644
--- a/prototorch/models/unsupervised.py
+++ b/prototorch/models/unsupervised.py
@@ -58,8 +58,10 @@ class KohonenSOM(NonGradientMixin, UnsupervisedPrototypeModel):
         diff = x.unsqueeze(dim=1) - protos
         delta = self._lr * self.hparams.alpha * nh.unsqueeze(-1) * diff
         updated_protos = protos + delta.sum(dim=0)
-        self.proto_layer.load_state_dict({"_components": updated_protos},
-                                         strict=False)
+        self.proto_layer.load_state_dict(
+            {"_components": updated_protos},
+            strict=False,
+        )
 
     def training_epoch_end(self, training_step_outputs):
         self._sigma = self.hparams.sigma * np.exp(
@@ -145,6 +147,8 @@ class GrowingNeuralGas(NeuralGas):
 
     def configure_callbacks(self):
         return [
-            GNGCallback(reduction=self.hparams.insert_reduction,
-                        freq=self.hparams.insert_freq)
+            GNGCallback(
+                reduction=self.hparams.insert_reduction,
+                freq=self.hparams.insert_freq,
+            )
         ]