--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/_decompositions.py2023-12-06 09:08:13.895012+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/_decompositions.py2023-12-06 09:11:58.776404+00:00@@ -186,21 +186,22 @@    src_dim = list(src_tensor.shape())    src_dim[dim] = torch.floor_divide(end - start, step)    src = torch.expand(src, src_dim)-    if (start == 0 and end == dim_size and step == 0):+    if start == 0 and end == dim_size and step == 0:        return input_tensor    mask = []    if start != 0:        mask.append(torch.ge(input_tensor_shape, start))    if end != dim_size:        mask.append(torch.ge(input_tensor_shape, end))    if step != 1:        mask.append(torch.eq(src_dim, 0))    src_val = torch.masked(mask, src_dim, 0)-    return torch.where(mask, src_val,input_tensor)+    return torch.where(mask, src_val, input_tensor)+def get_decompositions(    enable_experimental_decompositions: bool = False,) -> Dict[OpOverload, Callable[[Any], Any]]:    if enable_experimental_decompositions:--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/lowering/test_decompositions.py2023-12-06 09:08:13.915012+00:00+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/lowering/test_decompositions.py2023-12-06 09:12:02.062349+00:00@@ -418,11 +418,10 @@            0,            DECIMALS_OF_AGREEMENT,            f"MaxPool3d TRT outputs don't match with the original model.",        )-    def test_lowering_select_scatter_module(self):        class selectScatter(torch.nn.Module):            def __init__(self, *args, **kwargs) -> None:                super().__init__(*args, **kwargs)@@ -435,11 +434,10 @@            torch.ops.aten.lt.default,            torch.ops.aten.lt.default,            torch.ops.aten.expand.default,            torch.ops.aten.eq.default,            torch.ops.aten.where.default,-        }        unexpected_ops = {torch.ops.aten.select_scatter}        inputs = [torch.randn(2, 2), torch.ones(2)]@@ -485,7 +483,8 @@            0,            DECIMALS_OF_AGREEMENT,            f"Select_scatter TRT outputs don't match with the original model.",        )+if __name__ == "__main__":    run_tests()

--- /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/lowering/test_decompositions.py2023-12-19 18:39:51.699972+00:00+++ /home/runner/work/TensorRT/TensorRT/tests/py/dynamo/lowering/test_decompositions.py2023-12-19 18:41:49.917712+00:00@@ -418,11 +418,10 @@            0,            DECIMALS_OF_AGREEMENT,            f"MaxPool3d TRT outputs don't match with the original model.",        )-    def test_lowering_select_scatter_module(self):        class selectScatter(torch.nn.Module):            def __init__(self, *args, **kwargs) -> None:                super().__init__(*args, **kwargs)@@ -435,11 +434,10 @@            torch.ops.aten.lt.default,            torch.ops.aten.lt.default,            torch.ops.aten.expand.default,            torch.ops.aten.eq.default,            torch.ops.aten.where.default,-        }        unexpected_ops = {torch.ops.aten.select_scatter}        inputs = [torch.randn(2, 2), torch.ones(2)]@@ -485,7 +483,8 @@            0,            DECIMALS_OF_AGREEMENT,            f"Select_scatter TRT outputs don't match with the original model.",        )+if __name__ == "__main__":    run_tests()

--- /home/runner/work/TensorRT/TensorRT/examples/int8/training/vgg16/vgg16.py2024-02-20 19:59:59.374321+00:00+++ /home/runner/work/TensorRT/TensorRT/examples/int8/training/vgg16/vgg16.py2024-02-20 20:01:49.660284+00:00@@ -1,10 +1,11 @@"""# Reference- [Very Deep Convolutional Networks for Large-Scale Image Recognition](    https://arxiv.org/abs/1409.1556) (ICLR 2015)"""+import torchimport torch.nn as nnimport torch.nn.functional as Ffrom functools import reduce--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/_Device.py2024-02-20 19:59:59.382321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/_Device.py2024-02-20 20:01:49.759276+00:00@@ -30,16 +30,18 @@        gpu_id (int): Device ID for target GPU        dla_core (int): Core ID for target DLA core        allow_gpu_fallback (bool): Whether falling back to GPU if DLA cannot support an op should be allowed    """-    device_type: Optional[-        trt.DeviceType-    ] = None  #: Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.+    device_type: Optional[trt.DeviceType] = (+        None  #: Target device type (GPU or DLA). Set implicitly based on if dla_core is specified.+    )    gpu_id: int = -1  #: Device ID for target GPU    dla_core: int = -1  #: Core ID for target DLA core-    allow_gpu_fallback: bool = False  #: Whether falling back to GPU if DLA cannot support an op should be allowed+    allow_gpu_fallback: bool = (+        False  #: Whether falling back to GPU if DLA cannot support an op should be allowed+    )    def __init__(self, *args: Any, **kwargs: Any):        """__init__ Method for torch_tensorrt.Device        Device accepts one of a few construction patterns--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/_Input.py2024-02-20 19:59:59.382321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/_Input.py2024-02-20 20:01:49.959821+00:00@@ -26,16 +26,16 @@    class _ShapeMode(Enum):        STATIC = 0        DYNAMIC = 1-    shape_mode: Optional[-        _ShapeMode-    ] = None  #: Is input statically or dynamically shaped-    shape: Optional[-        Tuple[int, ...] | Dict[str, Tuple[int, ...]]-    ] = None  #: Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form ``{ "min_shape": Tuple, "opt_shape": Tuple, "max_shape": Tuple }``+    shape_mode: Optional[_ShapeMode] = (+        None  #: Is input statically or dynamically shaped+    )+    shape: Optional[Tuple[int, ...] | Dict[str, Tuple[int, ...]]] = (+        None  #: Either a single Tuple or a dict of tuples defining the input shape. Static shaped inputs will have a single tuple. Dynamic inputs will have a dict of the form ``{ "min_shape": Tuple, "opt_shape": Tuple, "max_shape": Tuple }``+    )    dtype: _enums.dtype = (        _enums.dtype.unknown    )  #: The expected data type of the input tensor (default: torch_tensorrt.dtype.float32)    _explicit_set_dtype: bool = False    format: _enums.TensorFormat = (--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/_compiler.py2024-02-20 19:59:59.382321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/_compiler.py2024-02-20 20:01:50.013227+00:00@@ -212,13 +212,13 @@        "precision": precision,        "debug": debug,        "device": device,        "workspace_size": workspace_size,        "min_block_size": min_block_size,-        "torch_executed_ops": torch_executed_ops-        if torch_executed_ops is not None-        else set(),+        "torch_executed_ops": (+            torch_executed_ops if torch_executed_ops is not None else set()+        ),        "pass_through_build_failures": pass_through_build_failures,        "max_aux_streams": max_aux_streams,        "version_compatible": version_compatible,        "optimization_level": optimization_level,        "use_python_runtime": use_python_runtime,--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py2024-02-20 19:59:59.382321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/_TRTInterpreter.py2024-02-20 20:01:50.235895+00:00@@ -26,13 +26,13 @@from packaging import version_LOGGER: logging.Logger = logging.getLogger(__name__)-TRT_INTERPRETER_CALL_PRE_OBSERVER: Observer[-    Callable[[torch.fx.GraphModule], None]-] = Observer("TRT_INTERPRETER_CALL_PRE_OBSERVER")+TRT_INTERPRETER_CALL_PRE_OBSERVER: Observer[Callable[[torch.fx.GraphModule], None]] = (+    Observer("TRT_INTERPRETER_CALL_PRE_OBSERVER")+)class UnsupportedOperatorException(RuntimeError):    pass@@ -90,13 +90,13 @@        self.input_specs_iter = 0        self._cur_node_name: Optional[str] = None        self._cur_node: Optional[torch.fx.Node] = None        self._input_names: List[str] = []        self._output_names: List[str] = []-        self._itensor_to_tensor_meta: Dict[-            trt.tensorrt.ITensor, TensorMetadata-        ] = dict()+        self._itensor_to_tensor_meta: Dict[trt.tensorrt.ITensor, TensorMetadata] = (+            dict()+        )        self.compilation_settings = compilation_settings        # Data types for TRT Module output Tensors        self.output_dtypes = output_dtypes--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py2024-02-20 19:59:59.382321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/conversion/converter_utils.py2024-02-20 20:01:50.278485+00:00@@ -322,17 +322,15 @@    else:        raise AssertionError(f"Cannot convert {input_val} to TRT constant")@overload-def get_positive_dim(dim: int, dim_size: int) -> int:-    ...+def get_positive_dim(dim: int, dim_size: int) -> int: ...@overload-def get_positive_dim(dim: Sequence[int], dim_size: int) -> Tuple[int, ...]:-    ...+def get_positive_dim(dim: Sequence[int], dim_size: int) -> Tuple[int, ...]: ...def get_positive_dim(    dim: Union[int, Sequence[int]], dim_size: int) -> Union[int, Tuple[int, ...]]:--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py2024-02-20 19:59:59.386321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/_decomposition_groups.py2024-02-20 20:01:50.623768+00:00@@ -5,13 +5,13 @@from torch._decomp import get_decompositions as get_torch_decompositionsfrom torch._ops import OpOverload, OpOverloadPacketaten = torch.ops.aten-_core_aten_decompositions: Dict[-    OpOverload, Callable[[Any], Any]-] = core_aten_decompositions()+_core_aten_decompositions: Dict[OpOverload, Callable[[Any], Any]] = (+    core_aten_decompositions()+)torch_enabled_decompositions: Set[Union[OpOverload, OpOverloadPacket]] = {    aten._adaptive_avg_pool2d_backward,    aten.addcdiv,    aten.addcdiv_,    aten.addcmul,@@ -179,13 +179,13 @@torch_disabled_decompositions: Set[Union[OpOverload, OpOverloadPacket]] = {    aten._softmax.default,}-ENABLED_TORCH_DECOMPOSITIONS: Dict[-    OpOverload, Callable[[Any], Any]-] = get_torch_decompositions(torch_enabled_decompositions)+ENABLED_TORCH_DECOMPOSITIONS: Dict[OpOverload, Callable[[Any], Any]] = (+    get_torch_decompositions(torch_enabled_decompositions)+)TORCH_TRT_DECOMPOSITIONS: Dict[OpOverload, Callable[[Any], Any]] = {}def check_decomp_set_invariants() -> None:    """Validates no overlap between enabled and disabled decomposition sets"""--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/passes/lower_linear.py2024-02-20 19:59:59.386321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/passes/lower_linear.py2024-02-20 20:01:50.628829+00:00@@ -20,16 +20,14 @@        logger.debug(f"Graph after lowering linear:\n{gm.graph}")    return gm-def linear_replacement() -> (-    Tuple[-        torch.fx.GraphModule,-        Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],-    ]-):+def linear_replacement() -> Tuple[+    torch.fx.GraphModule,+    Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],+]:    """Constructs the original and replacement functions for linear"""    # Original graph    def orig(        input: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/passes/view_to_reshape.py2024-02-20 19:59:59.386321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/passes/view_to_reshape.py2024-02-20 20:01:50.665412+00:00@@ -20,16 +20,14 @@        logger.debug(f"Graph after replacing view with reshape:\n{gm.graph}")    return gm-def view_replacement() -> (-    Tuple[-        torch.fx.GraphModule,-        Callable[[torch.Tensor, List[torch.SymInt]], torch.Tensor],-    ]-):+def view_replacement() -> Tuple[+    torch.fx.GraphModule,+    Callable[[torch.Tensor, List[torch.SymInt]], torch.Tensor],+]:    """Constructs the original and replacement functions for view"""    # Original graph    def orig(input: torch.Tensor, shape: List[torch.SymInt]) -> torch.Tensor:        return torch.ops.aten.view.default(input, shape)--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/passes/lower_scaled_dot_product_attention.py2024-02-20 19:59:59.386321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/passes/lower_scaled_dot_product_attention.py2024-02-20 20:01:50.681914+00:00@@ -58,16 +58,14 @@        logger.debug(f"Graph after lowering scaled dot product attention:\n{gm.graph}")    return gm-def scaled_dot_product_attention_replacement() -> (-    Tuple[-        Sequence[Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]],-        Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],-    ]-):+def scaled_dot_product_attention_replacement() -> Tuple[+    Sequence[Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor]],+    Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],+]:    """Constructs the original and replacement functions for efficient attention"""    # Efficient Attention original graph    def efficient(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:        outputs = torch.ops.aten._scaled_dot_product_efficient_attention.default(--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py2024-02-20 19:59:59.386321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/runtime/_PythonTorchTensorRTModule.py2024-02-20 20:01:50.959439+00:00@@ -99,25 +99,29 @@                self.engine.get_binding_dtype(idx), Frameworks.TORCH            )            for idx in self.output_binding_indices_in_order        ]        self.output_shapes = [-            tuple(self.engine.get_binding_shape(idx))-            if self.engine.has_implicit_batch_dimension-            else tuple()+            (+                tuple(self.engine.get_binding_shape(idx))+                if self.engine.has_implicit_batch_dimension+                else tuple()+            )            for idx in self.output_binding_indices_in_order        ]        self.hidden_output_dtypes = [            unified_dtype_converter(                self.engine.get_binding_dtype(idx), Frameworks.TORCH            )            for idx in self.hidden_output_binding_indices_in_order        ]        self.hidden_output_shapes = [-            tuple(self.engine.get_binding_shape(idx))-            if self.engine.has_implicit_batch_dimension-            else tuple()+            (+                tuple(self.engine.get_binding_shape(idx))+                if self.engine.has_implicit_batch_dimension+                else tuple()+            )            for idx in self.hidden_output_binding_indices_in_order        ]    def _check_initialized(self) -> None:        if not self.initialized:@@ -165,13 +169,15 @@        self.__dict__.update(state)        if self.engine:            self.context = self.engine.create_execution_context()    def forward(self, *inputs: torch.Tensor) -> torch.Tensor | Tuple[torch.Tensor, ...]:-        with torch.autograd.profiler.record_function(-            "PythonTorchTensorRTModule:Forward"-        ) if self.profiling_enabled else nullcontext():+        with (+            torch.autograd.profiler.record_function("PythonTorchTensorRTModule:Forward")+            if self.profiling_enabled+            else nullcontext()+        ):            self._check_initialized()            # If in safe mode, check at each iteration for for whether a switch is required            if (                torch_tensorrt.runtime.multi_device_safe_mode._PY_RT_MULTI_DEVICE_SAFE_MODE@@ -198,13 +204,17 @@                    torch.cuda.set_device(device_id)                    inputs = tuple([tensor.to(device) for tensor in inputs])                    logger.warning(f"Moved all input Tensors to cuda:{device_id}")-            with torch.autograd.profiler.record_function(-                "PythonTorchTensorRTModule:ProcessInputs"-            ) if self.profiling_enabled else nullcontext():+            with (+                torch.autograd.profiler.record_function(+                    "PythonTorchTensorRTModule:ProcessInputs"+                )+                if self.profiling_enabled+                else nullcontext()+            ):                assert len(inputs) == len(                    self.input_names                ), f"Wrong number of inputs, expect {len(self.input_names)} get {len(inputs)}."                contiguous_inputs: List[torch.Tensor] = [i.contiguous() for i in inputs]@@ -237,13 +247,17 @@                    self.context.set_binding_shape(                        idx, tuple(contiguous_inputs[i].shape)                    )-            with torch.autograd.profiler.record_function(-                "PythonTorchTensorRTModule:ProcessOutputs"-            ) if self.profiling_enabled else nullcontext():+            with (+                torch.autograd.profiler.record_function(+                    "PythonTorchTensorRTModule:ProcessOutputs"+                )+                if self.profiling_enabled+                else nullcontext()+            ):                # create output tensors                outputs: List[torch.Tensor] = []                for i, idx in enumerate(self.output_binding_indices_in_order):                    shape = tuple(self.context.get_binding_shape(idx))@@ -264,13 +278,17 @@                        dtype=self.hidden_output_dtypes[i],                        device=torch.cuda.current_device(),                    )                    bindings[idx] = output.data_ptr()-            with torch.autograd.profiler.record_function(-                "PythonTorchTensorRTModule:TensorRTRuntime"-            ) if self.profiling_enabled else nullcontext():+            with (+                torch.autograd.profiler.record_function(+                    "PythonTorchTensorRTModule:TensorRTRuntime"+                )+                if self.profiling_enabled+                else nullcontext()+            ):                self.context.execute_async_v2(                    bindings, torch.cuda.current_stream().cuda_stream                )            if len(outputs) == 1:--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/converters/aten_ops_converters.py2024-02-20 19:59:59.390321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/converters/aten_ops_converters.py2024-02-20 20:01:51.233651+00:00@@ -315,25 +315,21 @@    name: str,) -> Union[TRTTensor, Sequence[TRTTensor]]:    kwargs_new = {        "input": args[0],        "kernel_size": args[1],-        "stride": args[2]-        if len(args) > 2-        else (None, None)-        if len(args[1]) == 2-        else (None, None, None),-        "padding": args[3]-        if len(args) > 3-        else (0, 0)-        if len(args[1]) == 2-        else (0, 0, 0),-        "dilation": args[4]-        if len(args) > 4-        else (1, 1)-        if len(args[1]) == 2-        else (1, 1, 1),+        "stride": (+            args[2]+            if len(args) > 2+            else (None, None) if len(args[1]) == 2 else (None, None, None)+        ),+        "padding": (+            args[3] if len(args) > 3 else (0, 0) if len(args[1]) == 2 else (0, 0, 0)+        ),+        "dilation": (+            args[4] if len(args) > 4 else (1, 1) if len(args[1]) == 2 else (1, 1, 1)+        ),        "ceil_mode": args[5] if len(args) > 5 else False,    }    return acc_ops_converters.acc_ops_max_poolnd(        network, target, None, kwargs_new, name    )--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/lower.py2024-02-20 19:59:59.390321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/lower.py2024-02-20 20:01:51.283354+00:00@@ -124,25 +124,29 @@        interpreter = TRTInterpreter(            mod,            input_specs=self.lower_setting.input_specs,            explicit_batch_dimension=self.lower_setting.explicit_batch_dimension,            explicit_precision=self.lower_setting.explicit_precision,-            logger_level=trt.Logger.VERBOSE-            if self.lower_setting.verbose_log-            else trt.Logger.WARNING,+            logger_level=(+                trt.Logger.VERBOSE+                if self.lower_setting.verbose_log+                else trt.Logger.WARNING+            ),        )        interp_result: TRTInterpreterResult = interpreter.run(            max_batch_size=self.lower_setting.max_batch_size,            max_workspace_size=self.lower_setting.max_workspace_size,            lower_precision=self.lower_setting.lower_precision,            strict_type_constraints=self.lower_setting.strict_type_constraints,            algorithm_selector=algo_selector,            timing_cache=cache_data,-            profiling_verbosity=trt.ProfilingVerbosity.DETAILED-            if self.lower_setting.verbose_profile-            else trt.ProfilingVerbosity.LAYER_NAMES_ONLY,+            profiling_verbosity=(+                trt.ProfilingVerbosity.DETAILED+                if self.lower_setting.verbose_profile+                else trt.ProfilingVerbosity.LAYER_NAMES_ONLY+            ),            tactic_sources=self.lower_setting.tactic_sources,        )        # Update timing cache file if needed        timing_cache = interp_result.serialized_cache@@ -295,14 +299,12 @@                module.half()                # A custom conversion function can be passed to the lowerer to                # handle inputs with custom types. By default, just handle                # tensors and NoneType.                if fp16_conversion_fn is None:-                    conversion_fn = (-                        lambda x: x.half()-                        if x is not None and x.dtype == torch.float32-                        else x+                    conversion_fn = lambda x: (+                        x.half() if x is not None and x.dtype == torch.float32 else x                    )                else:                    conversion_fn = fp16_conversion_fn                inputs = tuple(conversion_fn(x) for x in inputs)--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/fx2trt.py2024-02-20 19:59:59.390321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/fx2trt.py2024-02-20 20:01:51.328023+00:00@@ -19,13 +19,13 @@from .observer import Observerfrom .utils import get_dynamic_dims, LowerPrecision, unified_dtype_converter, Frameworks_LOGGER: logging.Logger = logging.getLogger(__name__)-TRT_INTERPRETER_CALL_PRE_OBSERVER: Observer[-    Callable[[torch.fx.GraphModule], None]-] = Observer("TRT_INTERPRETER_CALL_PRE_OBSERVER")+TRT_INTERPRETER_CALL_PRE_OBSERVER: Observer[Callable[[torch.fx.GraphModule], None]] = (+    Observer("TRT_INTERPRETER_CALL_PRE_OBSERVER")+)class TRTInterpreterResult(NamedTuple):    engine: Any    input_names: Sequence[str]@@ -73,13 +73,13 @@        self.input_specs_iter = 0        self.validate_input_specs()        self._cur_node_name: Optional[str] = None        self._input_names: List[str] = []        self._output_names: List[str] = []-        self._itensor_to_tensor_meta: Dict[-            trt.tensorrt.ITensor, TensorMetadata-        ] = dict()+        self._itensor_to_tensor_meta: Dict[trt.tensorrt.ITensor, TensorMetadata] = (+            dict()+        )    def validate_input_specs(self):        for shape, _, _, shape_ranges, has_batch_dim in self.input_specs:            if not self.network.has_implicit_batch_dimension:                assert (--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/passes/lower_pass_manager_builder.py2024-02-20 19:59:59.390321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/passes/lower_pass_manager_builder.py2024-02-20 20:01:51.545029+00:00@@ -194,13 +194,15 @@                    lowering_start_time = datetime.datetime.now()                    self.lower_setting.input_specs = generate_input_specs(                        submod_inputs,                        self.lower_setting,-                        additional_submodule_inputs[submod_name]-                        if additional_submodule_inputs-                        else None,+                        (+                            additional_submodule_inputs[submod_name]+                            if additional_submodule_inputs+                            else None+                        ),                    )                    lowered_module = self._lower_func(                        submod, submod_inputs, self.lower_setting, submod_name                    )                    setattr(split_result.split_module, submod_name, lowered_module)@@ -234,13 +236,15 @@                if not submod_name.startswith(split_result.non_acc_submodule_prefix):                    _LOGGER.info(f"ACC submodule graph: {submod.graph}")                    lowering_start_time = datetime.datetime.now()                    self.lower_setting.additional_inputs = (-                        additional_submodule_inputs[submod_name]-                        if additional_submodule_inputs-                        else None,+                        (+                            additional_submodule_inputs[submod_name]+                            if additional_submodule_inputs+                            else None+                        ),                    )                    lowered_module = self._lower_func(                        submod, submod_inputs, self.lower_setting, submod_name                    )--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/passes/pass_utils.py2024-02-20 19:59:59.390321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/passes/pass_utils.py2024-02-20 20:01:51.722875+00:00@@ -193,13 +193,11 @@                kwargs2 = {"equal_nan": True}                if rtol:                    kwargs2["rtol"] = rtol                if atol:                    kwargs2["atol"] = atol-                kwargs2[-                    "msg"-                ] = (+                kwargs2["msg"] = (                    lambda msg: f"Pass {pass_} failed correctness check due at output {kk}:\n{msg}"                )                # If tensors are on different devices, make sure to compare                # their copies that are on the same device.                if x.get_device() != y.get_device():--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/passes/lower_basic_pass.py2024-02-20 19:59:59.390321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/passes/lower_basic_pass.py2024-02-20 20:01:51.782883+00:00@@ -536,13 +536,13 @@        reshape_batch_size: Optional[fx.Node] = get_reshape_batch_size_as_node(            maybe_reshape        )        if not reshape_batch_size:            continue-        reshape_batch_size_inferred_source: Optional[-            fx.Node-        ] = get_reshape_batch_size_inferred_source(reshape_batch_size)+        reshape_batch_size_inferred_source: Optional[fx.Node] = (+            get_reshape_batch_size_inferred_source(reshape_batch_size)+        )        if not reshape_batch_size_inferred_source:            continue        reshape_input: fx.Node = maybe_reshape.kwargs["input"]        if reshape_input == reshape_batch_size_inferred_source:--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/test/converters/acc_op/test_split.py2024-02-20 19:59:59.394321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/test/converters/acc_op/test_split.py2024-02-20 20:01:52.206806+00:00@@ -21,13 +21,15 @@        inputs = [torch.randn(1, 10)]        self.run_test(            Split(),            inputs,            expected_ops={-                acc_ops.split-                if isinstance(split_size_or_sections, int)-                else acc_ops.slice_tensor+                (+                    acc_ops.split+                    if isinstance(split_size_or_sections, int)+                    else acc_ops.slice_tensor+                )            },            test_explicit_batch_dim=False,        )    @parameterized.expand(@@ -68,13 +70,15 @@        ]        self.run_test_with_dynamic_shape(            Split(),            input_specs,            expected_ops={-                acc_ops.split-                if isinstance(split_size_or_sections, int)-                else acc_ops.slice_tensor+                (+                    acc_ops.split+                    if isinstance(split_size_or_sections, int)+                    else acc_ops.slice_tensor+                )            },        )    # Testing with (-1, -1, -1) results into following error:    # AssertionError: Can't chunk on dynamic shape dimension!--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/tools/common_fx2trt.py2024-02-20 19:59:59.394321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/tools/common_fx2trt.py2024-02-20 20:01:52.903172+00:00@@ -152,13 +152,13 @@            mod.eval()            if len(expected_ops):                self.assert_has_op(mod, expected_ops)            interpreter_result = interpreter.run(-                lower_precision=LowerPrecision.FP16-                if fp16_mode-                else LowerPrecision.FP32+                lower_precision=(+                    LowerPrecision.FP16 if fp16_mode else LowerPrecision.FP32+                )            )            trt_mod = TRTModule(                interpreter_result.engine,                interpreter_result.input_names,                interpreter_result.output_names,--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/trt_module.py2024-02-20 19:59:59.398321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/fx/trt_module.py2024-02-20 20:01:53.269384+00:00@@ -67,25 +67,29 @@                self.engine.get_binding_dtype(idx), Frameworks.TORCH            )            for idx in self.output_binding_indices_in_order        ]        self.output_shapes = [-            tuple(self.engine.get_binding_shape(idx))-            if self.engine.has_implicit_batch_dimension-            else tuple()+            (+                tuple(self.engine.get_binding_shape(idx))+                if self.engine.has_implicit_batch_dimension+                else tuple()+            )            for idx in self.output_binding_indices_in_order        ]        self.hidden_output_dtypes: Sequence[torch.dtype] = [            unified_dtype_converter(                self.engine.get_binding_dtype(idx), Frameworks.TORCH            )            for idx in self.hidden_output_binding_indices_in_order        ]        self.hidden_output_shapes = [-            tuple(self.engine.get_binding_shape(idx))-            if self.engine.has_implicit_batch_dimension-            else tuple()+            (+                tuple(self.engine.get_binding_shape(idx))+                if self.engine.has_implicit_batch_dimension+                else tuple()+            )            for idx in self.hidden_output_binding_indices_in_order        ]    def _check_initialized(self):        if not self.initialized:--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/ts/_compile_spec.py2024-02-20 19:59:59.398321+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/ts/_compile_spec.py2024-02-20 20:01:53.546949+00:00@@ -404,13 +404,13 @@        "inputs": inputs if inputs is not None else [],        # "input_signature": input_signature,        "device": device,        "disable_tf32": disable_tf32,  # Force FP32 layers to use traditional as FP32 format vs the default behavior of rounding the inputs to 10-bit mantissas before multiplying, but accumulates the sum using 23-bit mantissas        "sparse_weights": sparse_weights,  # Enable sparsity for convolution and fully connected layers.-        "enabled_precisions": enabled_precisions-        if enabled_precisions is not None-        else set(),  # Enabling FP16 kernels+        "enabled_precisions": (+            enabled_precisions if enabled_precisions is not None else set()+        ),  # Enabling FP16 kernels        "refit": refit,  # enable refit        "debug": debug,  # enable debuggable engine        "capability": capability,  # Restrict kernel selection to safe gpu kernels or safe dla kernels        "num_avg_timing_iters": num_avg_timing_iters,  # Number of averaging timing iterations used to select kernels        "workspace_size": workspace_size,  # Maximum size of workspace given to TensorRT

--- /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/_decompositions.py2024-02-27 08:54:58.869787+00:00+++ /home/runner/work/TensorRT/TensorRT/py/torch_tensorrt/dynamo/lowering/_decompositions.py2024-02-27 08:56:47.352375+00:00@@ -187,11 +187,11 @@    step_dim = (end - start) // step    end_dim = end    if step_dim > src_dim[dim]:        end_dim = src_dim[dim]    else:-        #In this case src first step_dim need to be selected+        # In this case src first step_dim need to be selected        indices = torch.Tensor(torch.arange(0, step_dim))        indices = indices.to(torch.int32)        src = torch.index_select(src_tensor, dim, indices)    if start == 0 and end == dim_size and step == 0: