correcting nn_ops for leaky_relu and correcting linting error

Author: apbose

py/torch_tensorrt/fx/converters/acc_ops_converters.py

@@ -1025,12 +1025,7 @@ def acc_ops_leaky_relu(
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
 
     return activation.leaky_relu(
-        network,
-        target,
-        SourceIR.ACC,
-        name,
-        kwargs["input"],
-        kwargs["negative_slope"]
+        network, target, SourceIR.ACC, name, kwargs["input"], kwargs["negative_slope"]
     )
 
 

py/torch_tensorrt/fx/converters/aten_ops_converters.py

@@ -216,7 +216,6 @@ def aten_ops_fmod(
     return acc_ops_converters.acc_ops_fmod(network, target, None, kwargs_new, name)
 
 
-
 @tensorrt_converter(torch.ops.aten.leaky_relu.default)
 def aten_ops_leaky_relu(
     network: TRTNetwork,
@@ -226,14 +225,7 @@ def aten_ops_leaky_relu(
     name: str,
 ) -> Union[TRTTensor, Sequence[TRTTensor]]:
 
-    return activation.leaky_relu(
-        network,
-        target,
-        SourceIR.ATEN,
-        name,
-        args[0],
-        args[1]
-    ) 
+    return activation.leaky_relu(network, target, SourceIR.ATEN, name, args[0], args[1])
 
 
 @tensorrt_converter(torch.ops.aten.linear)

py/torch_tensorrt/fx/converters/impl/activation.py

@@ -98,12 +98,14 @@ def leaky_relu(
     source_ir: Optional[SourceIR],
     name: str,
     input_val: TRTTensor,
-    alpha: Optional[Any]
+    alpha: Optional[Any],
 ):
     operation_type = trt.ActivationType.LEAKY_RELU
 
     def leaky_relu_dyn_range_fn(dyn_range):
-        return (max(0, dyn_range[0]) + alpha * min(0, dyn_range[0])), (max(0, dyn_range[1]) + alpha * min(0, dyn_range[1]))
+        return (max(0, dyn_range[0]) + alpha * min(0, dyn_range[0])), (
+            max(0, dyn_range[1]) + alpha * min(0, dyn_range[1])
+        )
 
     return convert_activation(
         network,

py/torch_tensorrt/fx/converters/nn_ops_converters.py

@@ -30,11 +30,11 @@ def relu(network, submod, args, kwargs, layer_name):
     # args/kwargs should have already been normalized to kwargs
     assert len(args) == 0
 
-    return activation.relu(
+    return activation.leaky_relu(
         network=network,
         target="torch.nn.functional.leaky_relu",
         source_ir=SourceIR.NN,
         name=layer_name,
         input_val=kwargs["input"],
-        alpha=kwargs["negative_slope"]
-    )
+        alpha=kwargs["negative_slope"],
+    )

py/torch_tensorrt/fx/test/converters/aten_op/test_leaky_relu_aten.py

@@ -50,4 +50,4 @@ def forward(self, x):
 
 
 if __name__ == "__main__":
-    run_tests()
+    run_tests()