formatPython (#1292)

engines/python/setup/djl_python/transformers_neuronx_scheduler/optimum_neuron_scheduler.py

@@ -285,19 +285,23 @@ def decode(self) -> List[Generation]:
                 if input_ids is None:
                     # Create blank inputs covering all slots (even empty ones)
                     input_ids = torch.full(
-                        [self.model.batch_size, 1], fill_value=self.tokenizer.eos_token_id, dtype=torch.int64
-                    )
+                        [self.model.batch_size, 1],
+                        fill_value=self.tokenizer.eos_token_id,
+                        dtype=torch.int64)
                 # input_ids are simply the tokens generated by the last decode or prefill requests (other tokens are cached)
                 input_ids[i, 0] = slot.next_token
                 if attention_mask is None:
                     # Create default mask covering all slots (even empty ones)
                     attention_mask = torch.zeros(
-                        [self.model.batch_size, slot.attention_mask.size(-1)], dtype=torch.int64
-                    )
+                        [self.model.batch_size,
+                         slot.attention_mask.size(-1)],
+                        dtype=torch.int64)
                     attention_mask[:, -1] = 1
                 attention_mask[i, :] = slot.attention_mask
         if input_ids is None:
-            raise ValueError("Unable to decode tokens for non-prefilled batches (probably due to a previous failure)")
+            raise ValueError(
+                "Unable to decode tokens for non-prefilled batches (probably due to a previous failure)"
+            )
         return self._generate_token(input_ids, attention_mask)
 
     def _generate_token(