feat(compatibility): Python3.6 compatibility

prototorch/models/abstract.py

@@ -1,7 +1,5 @@
 """Abstract classes to be inherited by prototorch models."""
 
-from typing import Final, final
-
 import pytorch_lightning as pl
 import torch
 import torchmetrics
@@ -43,7 +41,6 @@ class ProtoTorchBolt(pl.LightningModule):
         else:
             return optimizer
 
-    @final
     def reconfigure_optimizers(self):
         self.trainer.accelerator.setup_optimizers(self.trainer)
 
@@ -175,7 +172,7 @@ class NonGradientMixin(ProtoTorchMixin):
     """Mixin for custom non-gradient optimization."""
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.automatic_optimization: Final = False
+        self.automatic_optimization = False
 
     def training_step(self, train_batch, batch_idx, optimizer_idx=None):
         raise NotImplementedError
@@ -183,7 +180,6 @@ class NonGradientMixin(ProtoTorchMixin):
 
 class ImagePrototypesMixin(ProtoTorchMixin):
     """Mixin for models with image prototypes."""
-    @final
     def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
         """Constrain the components to the range [0, 1] by clamping after updates."""
         self.proto_layer.components.data.clamp_(0.0, 1.0)