Fix linter

backends/cadence/aot/fuse_ops.py

@@ -426,6 +426,9 @@ def fuse_quantized_batch_norm_with_conv(
         # Note: there is a quantized.conv2d.new operator in the resulting graph
         # that takes a torch.classes.quantized.Conv2dPackedParamsBase as one of the input
         # this prevents us to directly call graph_module.recompile().
+        # pyre-fixme[16]: `GraphModule` has no attribute `_code`.
+        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute
+        #  `python_code`.
         graph_module._code = graph_module._graph.python_code(root_module="self").src
 
     def __init__(self):

examples/models/llama/source_transformation/apply_spin_quant_r1_r2.py

@@ -148,7 +148,8 @@ def fuse_ln_linear(
             linear.bias.data = linear.bias.data.to(dtype=torch.float32) + torch.matmul(
                 # pyre-fixme[6]: For 2nd argument expected `Tensor` but got
                 #  `Union[Tensor, Module]`.
-                W_, layernorm.bias.to(dtype=torch.float32)
+                W_,
+                layernorm.bias.to(dtype=torch.float32),
             )
             linear.bias.data = linear.bias.data.to(linear_dtype)
 

exir/emit/_emitter.py

@@ -1635,7 +1635,8 @@ def plan(self) -> ExecutionPlan:
             # empty list.
             non_const_buffer_sizes=typing.cast(
                 # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorB...
-                List[int], self.module.meta["non_const_buffer_sizes"]
+                List[int],
+                self.module.meta["non_const_buffer_sizes"],
             ),
             container_meta_type=self.container_meta_type,
         )