def trace(
        self,
        root: Union[torch.nn.Module, Callable[..., Any]],
        concrete_args: Optional[Dict[str, Any]] = None,
        dummy_inputs: Optional[Dict[str, Any]] = None,
        complete_concrete_args_with_inputs_not_in_dummy_inputs: bool = True,
    ) -> Graph:
        """
        Traces `root` and returns the corresponding FX `torch.fx.Graph` representation. `root` can either be a
        `torch.nn.Module` instance or a Python callable. Note that after this call, `self.root` may be different from
        the `root` passed in here. For example, when a free function is passed to `trace()`, we will create a
        `torch.nn.Module` instance to use as the root and add embedded constants to.

        Args:
            root (`torch.nn.Module` or  `Callable`):
                Either a `torch.nn.Module`` or a function to be traced through. If root is not a
                [`~transformers.PreTrainedModel`], then `dummy_inputs` must be passed, otherwise tracing will fail.
            concrete_args (`Dict[str, Any], *optional*):
                Concrete arguments that should not be treated as Proxies
            dummy_inputs (`Dict[str, Any]`, *optional*):
                The dummy inputs needed to handle data-dependent control-flow if `root` is not a
                [`~transformers.PreTrainedModel`]. It can also be used when `root` is a
                [`~transformers.PreTrainedModel`] to specify custom dummy inputs for a subset or all the model inputs.
            complete_concrete_args_with_inputs_not_in_dummy_inputs (`bool`, *optional*, defaults to `True`):
                If `True`, and `dummy_inputs` is specified, every argument that `root` can take that is not in
                `dummy_inputs` and not in `concrete_args` will be added to `concrete_args`, otherwise does nothing.

        Returns:
            `torch.fx.Graph`:
                A FX `torch.fx.Graph` representing the semantics of the passed-in `root`.

        """
        sig = inspect.signature(root.forward if isinstance(root, torch.nn.Module) else root)

        if concrete_args is None:
            concrete_args = {}

        if dummy_inputs is not None and complete_concrete_args_with_inputs_not_in_dummy_inputs:
            for param in sig.parameters.values():
                if param.name in dummy_inputs:
                    continue
                if param.default is inspect.Parameter.empty:
                    raise ValueError(f"You need to specify a default value for the parameter {param.name}.")
            concrete_args.update(
                {
                    p.name: p.default
                    for p in sig.parameters.values()
                    if (p.name not in dummy_inputs and p.name not in concrete_args)
                }
            )

        input_names = sig.parameters.keys() - concrete_args.keys()

        # Creating a random input shape to generate dummy inputs.
        batch_size = _generate_random_int()
        sequence_length = _generate_random_int()
        shape = [batch_size, sequence_length]

        if root.__class__.__name__ in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES):
            num_choices = _generate_random_int(low=2, high=5)
            shape.insert(1, num_choices)

        inputs = dict(dummy_inputs) if dummy_inputs is not None else {}
        for input_name in input_names:
            if input_name in inputs:
                continue
            # We enforce that root must either be a PreTrainedModel or deserialized from a serialized traced model to
            # be able to use HFTracer._generate_dummy_input.
            if isinstance(root, self.supported_archs) or type(root).__qualname__.startswith(
                ("_deserialize_graph_module", "_CodeOnlyModule")
            ):
                inputs.update(self._generate_dummy_input(root, input_name, shape))
            else:
                raise RuntimeError(
                    f"Could not generate input named {input_name} for because root is not a"
                    " transformers.PreTrainedModel."
                )

        concrete_metas = {
            input_name: input_.to("meta") if isinstance(input_, torch.Tensor) else input_
            for input_name, input_ in inputs.items()
        }
        for param in sig.parameters.values():
            if param.kind == inspect.Parameter.VAR_KEYWORD and param.name not in input_names:
                concrete_metas[f"**{param.name}"] = {}
        self.meta_args = concrete_metas
        self.patched_torch_methods = {
            target: _gen_constructor_wrapper(getattr(torch, target)) for target in self._TORCH_METHODS_TO_PATCH
        }
        self.orig_fns = set()

        for name, (wrapper, orig) in self.patched_torch_methods.items():
            setattr(torch, name, wrapper)
            self.orig_fns.add(orig)

        try:
            self.graph = super().trace(root, concrete_args=concrete_args)
        finally:
            for name, (_, orig) in self.patched_torch_methods.items():
                setattr(torch, name, orig)

        # This is necessary because concrete args are added as input to the traced module since
        # https://github.com/pytorch/pytorch/pull/55888.
        for node in self.graph.nodes:
            if node.op == "placeholder":
                # Removing default values for inputs as the forward pass will fail with them.
                if node.target in input_names:
                    node.args = ()
                    # Without this, torch.jit.script fails because the inputs type is Optional[torch.Tensor].
                    # It cannot infer on the attributes and methods the input should have, and fails.
                    node.type = torch.Tensor
                # It is a concrete arg so it is not used and should be removed.
                else:
                    to_visit = [node]
                    to_delete = collections.OrderedDict()
                    while to_visit:
                        n = to_visit.pop(0)
                        to_delete[n] = None
                        to_visit += list(n.users.keys())

                    for user in reversed(to_delete.keys()):
                        self.graph.erase_node(user)

            # TODO: solves GraphModule creation.
            # Without this, return type annotation "Tuple" is causing code execution failure.
            if node.op == "output":
                node.type = None

        return self.graph

        Traces `root` and returns the corresponding FX `torch.fx.Graph` representation. `root` can either be a
        `torch.nn.Module` instance or a Python callable. Note that after this call, `self.root` may be different from
        the `root` passed in here. For example, when a free function is passed to `trace()`, we will create a
        `torch.nn.Module` instance to use as the root and add embedded constants to.

        Args:
            root (`torch.nn.Module` or  `Callable`):
                Either a `torch.nn.Module`` or a function to be traced through. If root is not a
                [`~transformers.PreTrainedModel`], then `dummy_inputs` must be passed, otherwise tracing will fail.
            concrete_args (`Dict[str, Any], *optional*):
                Concrete arguments that should not be treated as Proxies
            dummy_inputs (`Dict[str, Any]`, *optional*):
                The dummy inputs needed to handle data-dependent control-flow if `root` is not a
                [`~transformers.PreTrainedModel`]. It can also be used when `root` is a
                [`~transformers.PreTrainedModel`] to specify custom dummy inputs for a subset or all the model inputs.
            complete_concrete_args_with_inputs_not_in_dummy_inputs (`bool`, *optional*, defaults to `True`):
                If `True`, and `dummy_inputs` is specified, every argument that `root` can take that is not in
                `dummy_inputs` and not in `concrete_args` will be added to `concrete_args`, otherwise does nothing.

        Returns:
            `torch.fx.Graph`:
                A FX `torch.fx.Graph` representing the semantics of the passed-in `root`.

        
dpkd/transformers/src/transformers/utils/fx.py