chore: minor linting issue

Author: chohk88

py/torch_tensorrt/dynamo/conversion/aten_ops_converters.py

@@ -785,20 +785,20 @@ def aten_ops_select(
 
 
 def index_put_validator(node: Node) -> bool:
-    if args_bounds_check(node.args, 3, False): # Check if accumulate is valid
+    if args_bounds_check(node.args, 3, False):  # Check if accumulate is valid
         _LOGGER.debug("We do not support accumulate=True for aten.index_put operation")
         accumulate_valid = False
     else:
         accumulate_valid = True
-    
+
     # Retrieve input tensor's meta information
     input_meta = node.args[0].meta.get("tensor_meta")
     if not input_meta:
         _LOGGER.warning(
             "Meta information of input is missing. Unable to validate if broadcasting is needed, falling back to PyTorch operation."
         )
         return False
-    
+
     input_shape = input_meta.shape
     input_num_dims = len(input_shape)
 
@@ -807,9 +807,11 @@ def index_put_validator(node: Node) -> bool:
     if indices_num_dims == input_num_dims:
         broadcast_valid = True
     else:
-        _LOGGER.debug("We do not support broadcasting when the number of index dimensions does not match the number of input tensor dimensions.")
+        _LOGGER.debug(
+            "We do not support broadcasting when the number of index dimensions does not match the number of input tensor dimensions."
+        )
         broadcast_valid = False
-    
+
     # Return validation result
     return accumulate_valid and broadcast_valid
 

tests/py/dynamo/conversion/test_index_put_aten.py

@@ -140,7 +140,7 @@ class TestIndexPutConverter(DispatchTestCase):
             #     test_name="3d_indices_float_broadcase_index",
             #     source_tensor=torch.zeros([3, 3, 3], dtype = torch.int32),
             #     indices_tensor=(
-            #         torch.tensor([0,1], dtype=torch.int32), 
+            #         torch.tensor([0,1], dtype=torch.int32),
             #         torch.tensor([0,1], dtype=torch.int32),
             #     ),
             #     value_tensor=torch.tensor([10],  dtype = torch.int32),