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# Source code for torch.fx._symbolic_trace

    import builtins
    import copy
    import functools
    import inspect
    import math
    import os
    import warnings
    import collections
    from itertools import chain
    from types import CodeType, FunctionType, ModuleType
    from typing import (
        Any,
        Callable,
        Dict,
        List,
        NamedTuple,
        Optional,
        Set,
        Tuple,
        Type,
        Union,
    )

    import torch
    import torch.utils._pytree as pytree
    from torch._C import ScriptObject  # type: ignore[attr-defined]

    from ._compatibility import compatibility
    from .graph import _PyTreeCodeGen, _PyTreeInfo, Graph
    from .graph_module import GraphModule
    from .node import Argument, base_types, map_aggregate
    from .proxy import ParameterProxy, Proxy, TracerBase, Scope, ScopeContextManager

    HAS_VARSTUFF = inspect.CO_VARARGS | inspect.CO_VARKEYWORDS

    # These need to run in global scope to handle nested calls correctly
    _orig_module_call: Callable = torch.nn.Module.__call__
    _orig_module_getattr: Callable = torch.nn.Module.__getattr__

    _proxyable_classes: Dict[Type, None] = {}

    _is_fx_tracing_flag = False

    def is_fx_tracing():
        return _is_fx_tracing_flag

    @compatibility(is_backward_compatible=True)
    class ProxyableClassMeta(type):
        """
        ProxyableClassMeta allows you to make construction of a given Python class
        symbolically traceable. For example::

            import torch
            import torch.fx

            class TensorPair(metaclass=torch.fx.ProxyableClassMeta):
                def __init__(self, left, right):
                    self.left, self.right = left, right

                def add(self, other):
                    l = self.left + other.left
                    r = self.right + other.right
                    return TensorPair(l, r)

                def mul(self, other):
                    l = self.left * other.left
                    r = self.right * other.right
                    return TensorPair(l, r)

            def use_tensor_pair_ctor(x : TensorPair, y : torch.Tensor):
                s = x.add(TensorPair(y, y))
                return s.mul(x)

            x = TensorPair(torch.randn(5, 3), torch.randn(5, 3))
            y = torch.randn(5, 3)
            ref_out = use_tensor_pair_ctor(x, y)

            traced = torch.fx.symbolic_trace(use_tensor_pair_ctor)
            print(traced.code)
            '''
            def forward(self, x : __main___TensorPair, y : torch.Tensor):
                tensor_pair = __main___TensorPair(y, y);  y = None
                add = x.add(tensor_pair);  tensor_pair = None
                mul = add.mul(x);  add = x = None
                return mul
            '''

        From this example, we can see that construction of a class (``TensorPair``)
        defined with ``ProxyableClassMeta`` as metaclass can be recorded in symbolic
        tracing.
        """

        def __init__(cls, name, bases, attrs):
            _proxyable_classes.setdefault(cls)
            super().__init__(name, bases, attrs)

        def __call__(cls, *args, **kwargs):
            instance = cls.__new__(cls)  # type: ignore[call-overload]

            found_proxies = []

            def check_proxy(a):
                if isinstance(a, Proxy):
                    found_proxies.append(a)

            map_aggregate(args, check_proxy)
            map_aggregate(kwargs, check_proxy)

            if len(found_proxies) != 0:
                tracer = found_proxies[0].tracer
                return tracer.create_proxy("call_function", cls, args, kwargs)
            else:
                cls.__init__(instance, *args, **kwargs)  # type: ignore[misc]
                return instance

    def _patch_function(fn: FunctionType, nargs: int) -> FunctionType:
        co = fn.__code__
        co_flags = co.co_flags & ~HAS_VARSTUFF
        co_args: tuple
        if hasattr(co, "co_qualname"):
            # Python-3.11+ code signature
            co_args = (
                nargs,
                0,
                0,
                co.co_nlocals,
                co.co_stacksize,
                co_flags,
                co.co_code,
                co.co_consts,
                co.co_names,
                co.co_varnames,
                co.co_filename,
                co.co_name,
                co.co_qualname,  # type: ignore[attr-defined]
                co.co_firstlineno,
                co.co_lnotab,
                co.co_exceptiontable,  # type: ignore[attr-defined]
                co.co_freevars,
                co.co_cellvars,
            )
        elif hasattr(co, "co_posonlyargcount"):
            co_args = (
                nargs,
                0,
                0,
                co.co_nlocals,
                co.co_stacksize,
                co_flags,
                co.co_code,
                co.co_consts,
                co.co_names,
                co.co_varnames,
                co.co_filename,
                co.co_name,
                co.co_firstlineno,
                co.co_lnotab,
                co.co_freevars,
                co.co_cellvars,
            )
        else:
            co_args = (
                nargs,
                0,
                co.co_nlocals,
                co.co_stacksize,
                co_flags,
                co.co_code,
                co.co_consts,
                co.co_names,
                co.co_varnames,
                co.co_filename,
                co.co_name,
                co.co_firstlineno,
                co.co_lnotab,
                co.co_freevars,
                co.co_cellvars,
            )
        new_code = CodeType(*co_args)  # type: ignore[arg-type]
        return FunctionType(
            new_code, fn.__globals__, fn.__name__, fn.__defaults__, fn.__closure__
        )

        # we need to insert placeholder nodes for *args and **kwargs
        # we can't call this function normally, otherwise it would try to unpack them
        # instead, let's make python think that args and kwargs are normal variables

    @compatibility(is_backward_compatible=False)
    class PHBase:
        """
        Object representing an input placeholder to `concrete_args`
        """

        def __repr__(self):
            return "PH"

    PH = PHBase()

    @compatibility(is_backward_compatible=False)
    class PHWithMeta(PHBase):
        """
        Object representing an input placeholder to `concrete_args`
        """
        def __init__(self, ph_key: Optional[str] = None):
            super().__init__()

            # Provide a hey for user to identify placeholder node during analysis
            self.ph_key = ph_key

    @compatibility(is_backward_compatible=True)
    class Tracer(TracerBase):
        # Reference: https://github.com/pytorch/pytorch/issues/54354
        # The first line of this docstring overrides the one Sphinx generates for the
        # documentation. We need it so that Sphinx doesn't leak `math`s path from the
        # build environment (e.g. `<module 'math' from '/leaked/path').

        """Tracer(autowrap_modules=(math,), autowrap_functions=())

        ``Tracer`` is the class that implements the symbolic tracing functionality
        of ``torch.fx.symbolic_trace``. A call to ``symbolic_trace(m)`` is equivalent
        to ``Tracer().trace(m)``.

        Tracer can be subclassed to override various behaviors of the tracing
        process. The different behaviors that can be overridden are described
        in the docstrings of the methods on this class.
        """

        # Not checking BC on this API because the default value for `autowrap_modules`
        # includes the local filepath to the `math` module, which would jitter
        # across machines.
        @compatibility(is_backward_compatible=True)
        def __init__(
            self,
            autowrap_modules: Tuple[ModuleType] = (math,),
            autowrap_functions: Tuple[Callable, ...] = (),
            param_shapes_constant: bool = False,
        ) -> None:
            # This method's signature is overridden by the first line of this class'
            # docstring. If this method's signature is modified, the signature that
            # overrides it also should be modified accordingly.

            """
            Construct a Tracer object.

            Args:

                autowrap_modules (Tuple[ModuleType]): defaults to `(math, )`,
                    Python modules whose functions should be wrapped automatically
                    without needing to use fx.wrap(). Backward-compatibility for
                    this parameter is guaranteed.

                autowrap_functions (Tuple[Callable, ...]): defaults to `()`,
                    Python functions that should be wrapped automatically without
                    needing to use fx.wrap(). Backward compatibility for this
                    parameter is guaranteed.

                param_shapes_constant (bool): When this flag is set,  calls to shape,
                    size and a few other shape like attributes of a module's parameter
                    will be evaluated directly, rather than returning a new Proxy value
                    for an attribute access. Backward compatibility for this parameter
                    is guaranteed.
            """

            super().__init__()

            # Functions we will eagerly wrap when we see them while tracing
            # this captures both `math.sqrt()` and `from math import sqrt` automatically
            self._autowrap_function_ids: Set[int] = {
                id(value)
                for name, value in chain(*[m.__dict__.items() for m in autowrap_modules])
                if not name.startswith("_") and callable(value)
            }
            self._autowrap_function_ids.update({id(f) for f in autowrap_functions})

            # Python modules to apply autowrap to at the start, in addition to
            # modules we see while tracing
            self._autowrap_search: List[ModuleType] = list(autowrap_modules)
            self.param_shapes_constant = param_shapes_constant

            self.submodule_paths: Optional[Dict[torch.nn.Module, str]] = None
            self.root_module_name: str = ""
            # Maps the containing module's name to the operator name
            self.scope = Scope("", None)
            # Records the module call stack
            self.module_stack = collections.OrderedDict()
            # Mapping of node name to module scope
            self.node_name_to_scope: Dict[str, Tuple[str, type]] = {}

        @compatibility(is_backward_compatible=True)
        def create_arg(self, a: Any) -> "Argument":
            """
            A method to specify the behavior of tracing when preparing values to
            be used as arguments to nodes in the ``Graph``.

            By default, the behavior includes:

            #. Iterate through collection types (e.g. tuple, list, dict) and recursively
               call ``create_args`` on the elements.
            #. Given a Proxy object, return a reference to the underlying IR ``Node``
            #. Given a non-Proxy Tensor object, emit IR for various cases:

                * For a Parameter, emit a ``get_attr`` node referring to that Parameter
                * For a non-Parameter Tensor, store the Tensor away in a special
                  attribute referring to that attribute.

            This method can be overridden to support more types.

            Args:

                a (Any): The value to be emitted as an ``Argument`` in the ``Graph``.

            Returns:

                The value ``a`` converted into the appropriate ``Argument``
            """
            # The base tracer is used to construct Graphs when there is no associated
            # module hierarchy, so it can never create parameter references.
            # The default tracer adds the ability to refer to parameters when
            # tracing modules.
            if isinstance(a, torch.nn.Parameter):
                for n, p in self.root.named_parameters():
                    if a is p:
                        return self.create_node("get_attr", n, (), {})
                raise NameError("parameter is not a member of this module")
            elif isinstance(a, torch.Tensor):
                for n_, p_ in self.root.named_buffers():
                    if a is p_:
                        return self.create_node("get_attr", n_, (), {})
            elif isinstance(a, torch.nn.Module):
                for n_, p_ in self.root.named_modules():
                    if a is p_:
                        return self.create_node("get_attr", n_, (), {})
            # For NamedTuple instances that appear literally as args, we emit
            # a node to construct the NamedTuple and use that Node as the argument.
            if isinstance(a, tuple) and hasattr(a, "_fields"):
                args = tuple(self.create_arg(elem) for elem in a)
                return self.create_node("call_function", a.__class__, args, {})

            # Tensors do not have a reliable string repr() from which they can be
            # constructed (and we probably don't want to rely on that, either), so
            # for any constant Tensor values we encounter, first search for if they
            # are an attribute of some module in the module hierarchy. If so, emit
            # a get_attr to retrieve that tensor. Otherwise, we'll store away the
            # tensor value into a special attribute on the Module s.t. we can
            # retrieve it with a get_attr.
            if isinstance(a, (torch.Tensor, ScriptObject)):
                qualname: Optional[str] = self.tensor_attrs.get(a)

                # Tensor was not found in the Module hierarchy, stow it away in a
                # special attribute and set the qualname to refer to that
                if not qualname:
                    i = 0
                    while True:
                        qualname = f"_tensor_constant{i}"
                        if not hasattr(self.root, qualname):
                            break
                        i += 1
                    self.tensor_attrs[a] = qualname
                    setattr(self.root, qualname, a)

                return self.create_node("get_attr", qualname, (), {})

            if type(a) in _proxyable_classes:
                # This is an instance of a proxyable class for which we did not
                # witness its construction. Intern this as a constant attribute

                # TODO: binary search
                i = 0
                while True:
                    qualname = f"_{a.__class__.__name__}_constant_{i}"
                    if not hasattr(self.root, qualname):
                        break
                    i += 1
                setattr(self.root, qualname, a)

                return self.create_node("get_attr", qualname, (), {})

            return super().create_arg(a)

        @compatibility(is_backward_compatible=True)
        def is_leaf_module(self, m: torch.nn.Module, module_qualified_name: str) -> bool:
            """
            A method to specify whether a given ``nn.Module`` is a "leaf" module.

            Leaf modules are the atomic units that appear in
            the IR, referenced by ``call_module`` calls. By default,
            Modules in the PyTorch standard library namespace (torch.nn)
            are leaf modules. All other modules are traced through and
            their constituent ops are recorded, unless specified otherwise
            via this parameter.

            Args:

                m (Module): The module being queried about
                module_qualified_name (str): The path to root of this module. For example,
                    if you have a module hierarchy where submodule ``foo`` contains
                    submodule ``bar``, which contains submodule ``baz``, that module will
                    appear with the qualified name ``foo.bar.baz`` here.
            """
            return (
                (m.__module__.startswith("torch.nn") or m.__module__.startswith("torch.ao.nn"))
                and not isinstance(m, torch.nn.Sequential)
            )

        @compatibility(is_backward_compatible=True)
        def path_of_module(self, mod: torch.nn.Module) -> str:
            """
            Helper method to find the qualified name of ``mod`` in the Module hierarchy
            of ``root``. For example, if ``root`` has a submodule named ``foo``, which has
            a submodule named ``bar``, passing ``bar`` into this function will return
            the string "foo.bar".

            Args:

                mod (str): The ``Module`` to retrieve the qualified name for.
            """
            # Prefer the O(1) algorithm
            if self.submodule_paths:
                path = self.submodule_paths.get(mod)
                if path is None:
                    raise NameError("module is not installed as a submodule")
                assert isinstance(path, str)
                return path
            # O(N^2) fallback in the case that we didn't store the submodule
            # paths.
            else:
                for n, p in self.root.named_modules():
                    if mod is p:
                        return n
                raise NameError("module is not installed as a submodule")

        @compatibility(is_backward_compatible=True)
        def call_module(
            self,
            m: torch.nn.Module,
            forward: Callable[..., Any],
            args: Tuple[Any, ...],
            kwargs: Dict[str, Any],
        ) -> Any:
            """
            Method that specifies the behavior of this ``Tracer`` when it encounters
            a call to an ``nn.Module`` instance.

            By default, the behavior is to check if the called module is a leaf module
            via ``is_leaf_module``. If it is, emit a ``call_module`` node referring to
            ``m`` in the ``Graph``. Otherwise, call the ``Module`` normally, tracing through
            the operations in its ``forward`` function.

            This method can be overridden to--for example--create nested traced
            GraphModules, or any other behavior you would want while tracing across
            ``Module`` boundaries.

            Args:

                m (Module): The module for which a call is being emitted
                forward (Callable): The forward() method of the ``Module`` to be invoked
                args (Tuple): args of the module callsite
                kwargs (Dict): kwargs of the module callsite

            Return:

                The return value from the Module call. In the case that a ``call_module``
                node was emitted, this is a ``Proxy`` value. Otherwise, it is whatever
                value was returned from the ``Module`` invocation.
            """
            module_qualified_name = self.path_of_module(m)
            with ScopeContextManager(self.scope, Scope(module_qualified_name, type(m))) as _scope:
                # module_stack is an ordered dict so writing then deleting the
                # entry is equivalent to push/pop on a list
                self.module_stack[_scope.module_path] = _scope.module_type
                if not self.is_leaf_module(m, module_qualified_name):
                    ret_val = forward(*args, **kwargs)
                else:
                    ret_val = self.create_proxy("call_module", module_qualified_name, args, kwargs)
                key, _ = self.module_stack.popitem(last=True)
                assert key == _scope.module_path, f" Unexpected key {key}"

            return ret_val

        @compatibility(is_backward_compatible=False)
        def getattr(self, attr: str, attr_val: Any, parameter_proxy_cache: Dict[str, Any]):
            """
            Method that specifies the behavior of this ``Tracer`` when we call getattr
            on a call to an ``nn.Module`` instance.

            By default, the behavior is to return a proxy value for the attribute. It
            also stores the proxy value in the ``parameter_proxy_cache``, so that future
            calls will reuse the proxy rather than creating a new one.

            This method can be overridden to --for example-- not return proxies when
            querying parameters.

            Args:

                attr (str): The name of the attribute being queried
                attr_val (Any): The value of the attribute
                parameter_proxy_cache (Dict[str, Any]): A cache of attr names to proxies

            Return:

                The return value from the getattr call.
            """
            def maybe_get_proxy_for_attr(
                attr_val, collection_to_search, parameter_proxy_cache
            ):
                for n, p in collection_to_search:
                    if attr_val is p:
                        if n not in parameter_proxy_cache:
                            kwargs = {}
                            if (
                                "proxy_factory_fn"
                                in inspect.signature(self.create_proxy).parameters
                            ):
                                kwargs["proxy_factory_fn"] = (
                                    None
                                    if not self.param_shapes_constant
                                    else lambda node: ParameterProxy(
                                        self, node, n, attr_val
                                    )
                                )
                            val_proxy = self.create_proxy("get_attr", n, (), {}, **kwargs)  # type: ignore[arg-type]
                            parameter_proxy_cache[n] = val_proxy
                        return parameter_proxy_cache[n]
                return None

            if isinstance(attr_val, torch.nn.Parameter):
                maybe_parameter_proxy = maybe_get_proxy_for_attr(
                    attr_val, self.root.named_parameters(), parameter_proxy_cache
                )
                if maybe_parameter_proxy is not None:
                    return maybe_parameter_proxy

            if self.proxy_buffer_attributes and isinstance(attr_val, torch.Tensor):
                maybe_buffer_proxy = maybe_get_proxy_for_attr(
                    attr_val, self.root.named_buffers(), parameter_proxy_cache
                )
                if maybe_buffer_proxy is not None:
                    return maybe_buffer_proxy

            return attr_val

        # This method will be refactored
        @compatibility(is_backward_compatible=False)
        def create_args_for_root(self, root_fn, is_module, concrete_args=None):
            """
            Create ``placeholder`` nodes corresponding to the signature of the ``root``
            Module. This method introspects root's signature and emits those
            nodes accordingly, also supporting ``*args`` and ``**kwargs``.
            """
            # In some cases, a function or method has been decorated with a wrapper
            # defined via ``functools.wraps``. In this case, the outer code object
            # will likely not contain the actual parameters we care about, so unwrap
            # the function to get to the innermost callable.
            fn_for_analysis = inspect.unwrap(root_fn)
            co = fn_for_analysis.__code__
            total_args = co.co_argcount + co.co_kwonlyargcount
            orig_args = list(co.co_varnames)
            names_iter = iter(co.co_varnames)
            args: List[Any] = []
            skip_arg_idx = 0
            if is_module:
                if total_args == 0:
                    raise RuntimeError(
                        "``self`` argument cannot be part of *args expansion!"
                    )
                skip_arg_idx = 1
                next(names_iter)  # skip self
                args.append(self.root)

            sig = inspect.signature(fn_for_analysis)

            def proxy_placeholder(name: str):
                if concrete_args is not None and name in concrete_args:
                    cnt = 0

                    def replace_ph(x):
                        nonlocal cnt
                        cnt += 1
                        param = sig.parameters[name]
                        default = (
                            ()
                            if param.default is inspect.Parameter.empty
                            else (param.default,)
                        )
                        out = self.create_proxy(
                            "placeholder", f"{name}_{str(cnt)}", default, {}
                        )
                        if isinstance(x, PHBase):
                            def transfer_attrs(fr, to):
                                for attr_name in dir(fr):
                                    attr_val = getattr(fr, attr_name)
                                    if (
                                        not callable(attr_val)
                                        and not attr_name.startswith("__")
                                        and not hasattr(to, attr_name)
                                    ):
                                        setattr(to, attr_name, attr_val)

                            if x != PH:
                                # Transfer attrs in the case where you're using a placeholder other
                                # than the singleton PH (PH has no attributes to transfer).
                                # Proxies were created out of the placeholders.
                                # Transfer any metadata (put on the placeholders in the form of
                                # attributes set by the user) from the placeholder to the
                                # underlying nodes (the proxy is unwrapped by the user, but
                                # the metadata should hold).
                                transfer_attrs(fr=x, to=out.node)

                            return out
                        # Union[int, bool] == bool in Python <= 3.6
                        if (
                            type(x) == bool
                            or type(x) in base_types
                            and type(x) != torch.Tensor
                        ):
                            torch._assert(
                                out == x,
                                f"{name} has been specialized to have value {x} but got another value",
                            )
                        elif type(x) == type(None):
                            args = (
                                out,
                                f"{name} has been specialized to have value None but got another value",
                            )
                            self.create_proxy("call_function", _assert_is_none, args, {})
                        else:
                            warnings.warn(
                                f"Was not able to add assertion to guarantee correct input {name} to "
                                f"specialized function. It is up to the user to make sure that your inputs match the "
                                f"inputs you specialized the function with."
                            )

                        return x

                    return pytree.tree_map(replace_ph, concrete_args[name])
                if name[0] == "*":
                    default = ()
                else:
                    param = sig.parameters[name]
                    default = () if param.default is inspect.Parameter.empty else (param.default,)  # type: ignore[assignment]
                return self.create_proxy(
                    "placeholder",
                    name,
                    default,
                    {},
                    type_expr=fn_for_analysis.__annotations__.get(name, None)
                )

            arg_names = [next(names_iter) for idx in range(skip_arg_idx, total_args)]
            if isinstance(concrete_args, tuple):
                if len(arg_names) != len(concrete_args):
                    raise RuntimeError(
                        f"Tracing expected {len(arg_names)} arguments but got {len(concrete_args)} concrete arguments"
                    )
                concrete_args = dict(zip(arg_names, concrete_args))
            args.extend(proxy_placeholder(names) for names in arg_names)

            if co.co_kwonlyargcount > 0 or co.co_flags & HAS_VARSTUFF:
                # TODO: type annotations for *args and **kwargs
                if co.co_flags & inspect.CO_VARARGS:
                    args.append(proxy_placeholder("*" + next(names_iter)))
                if co.co_flags & inspect.CO_VARKEYWORDS:
                    args.append(proxy_placeholder("**" + next(names_iter)))
                root_fn = _patch_function(root_fn, len(args))

            flat_args, in_spec = pytree.tree_flatten(tuple(args))
            if any(not isinstance(i, pytree.LeafSpec) for i in in_spec.children_specs):
                # In the case that we have pytree-flattened inputs in
                # `concrete_args`, generate a flattening wrapper around the
                # original root function and return that.
                self.graph._codegen = _PyTreeCodeGen(
                    _PyTreeInfo(orig_args[:total_args], in_spec, None)
                )

                def flatten_fn(*args):
                    tree_args = pytree.tree_unflatten(list(args), in_spec)
                    tree_out = root_fn(*tree_args)
                    out_args, out_spec = pytree.tree_flatten(tree_out)
                    assert isinstance(self.graph._codegen, _PyTreeCodeGen)
                    self.graph._codegen.pytree_info = (
                        self.graph._codegen.pytree_info._replace(out_spec=out_spec)
                    )
                    return out_args

                return flatten_fn, flat_args
            return root_fn, args

        @compatibility(is_backward_compatible=True)
        def trace(
            self,
            root: Union[torch.nn.Module, Callable[..., Any]],
            concrete_args: Optional[Dict[str, Any]] = None,
        ) -> Graph:
            """
            Trace ``root`` and return the corresponding FX ``Graph`` representation. ``root``
            can either be an ``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 an ``nn.Module`` instance to use as the root and add embedded constants
            to.

            Args:

                root (Union[Module, Callable]): Either a ``Module`` or a function to be
                    traced through. Backwards-compatibility for this parameter is
                    guaranteed.
                concrete_args (Optional[Dict[str, any]]): Concrete arguments that should
                    not be treated as Proxies. This parameter is experimental and
                    its backwards-compatibility is *NOT* guaranteed.

            Returns:

                A ``Graph`` representing the semantics of the passed-in ``root``.
            """
            global _is_fx_tracing_flag
            old_is_fx_tracing_flag = _is_fx_tracing_flag
            _is_fx_tracing_flag = True
            try:
                if isinstance(root, torch.nn.Module):
                    self.root = root

                    assert hasattr(
                        type(root), self.traced_func_name
                    ), f"traced_func_name={self.traced_func_name} doesn't exist in {type(root).__name__}"

                    fn = getattr(type(root), self.traced_func_name)
                    self.root_module_name = root._get_name()
                    self.submodule_paths = {mod: name for name, mod in root.named_modules()}
                else:
                    self.root = torch.nn.Module()
                    fn = root

                tracer_cls: Optional[Type[Tracer]] = getattr(self, "__class__", None)
                self.graph = Graph(tracer_cls=tracer_cls)
                if hasattr(fn, '__code__'):
                    code = fn.__code__
                    self.graph._co_fields = {
                        'co_name': code.co_name,
                        'co_filename': code.co_filename,
                        'co_firstlineno': code.co_firstlineno,
                    }

                # When we encounter a Tensor value that's not a parameter, we look if it
                # is some other attribute on the model. Construct a dict mapping Tensor
                # values to the qualified name here for efficiency. This is used downstream
                # in create_arg
                self.tensor_attrs: Dict[Union[torch.Tensor, ScriptObject], str] = {}

                def collect_tensor_attrs(m: torch.nn.Module, prefix_atoms: List[str]):
                    for k, v in m.__dict__.items():
                        if isinstance(v, (torch.Tensor, ScriptObject)):
                            self.tensor_attrs[v] = ".".join(prefix_atoms + [k])
                    for k, v in m.named_children():
                        collect_tensor_attrs(v, prefix_atoms + [k])

                collect_tensor_attrs(self.root, [])

                assert isinstance(fn, FunctionType)

                fn_globals = fn.__globals__  # run before it gets patched
                fn, args = self.create_args_for_root(
                    fn, isinstance(root, torch.nn.Module), concrete_args
                )

                parameter_proxy_cache: Dict[
                    str, Proxy
                ] = {}  # Reduce number of get_attr calls

                # Method dispatch on parameters is not recorded unless it's directly used.
                # Thus, we need to insert a proxy when __getattr__ requests a parameter.
                @functools.wraps(_orig_module_getattr)
                def module_getattr_wrapper(mod, attr):
                    attr_val = _orig_module_getattr(mod, attr)
                    return self.getattr(attr, attr_val, parameter_proxy_cache)

                @functools.wraps(_orig_module_call)
                def module_call_wrapper(mod, *args, **kwargs):
                    def forward(*args, **kwargs):
                        return _orig_module_call(mod, *args, **kwargs)

                    _autowrap_check(
                        patcher,
                        getattr(getattr(mod, "forward", mod), "__globals__", {}),
                        self._autowrap_function_ids,
                    )
                    return self.call_module(mod, forward, args, kwargs)

                with _Patcher() as patcher:
                    # allow duplicate patches to support the case of nested calls
                    patcher.patch_method(
                        torch.nn.Module,
                        "__getattr__",
                        module_getattr_wrapper,
                        deduplicate=False,
                    )
                    patcher.patch_method(
                        torch.nn.Module, "__call__", module_call_wrapper, deduplicate=False
                    )
                    _patch_wrapped_functions(patcher)
                    _autowrap_check(patcher, fn_globals, self._autowrap_function_ids)
                    for module in self._autowrap_search:
                        _autowrap_check(
                            patcher, module.__dict__, self._autowrap_function_ids
                        )
                    self.create_node(
                        "output",
                        "output",
                        (self.create_arg(fn(*args)),),
                        {},
                        type_expr=fn.__annotations__.get("return", None),
                    )

                self.submodule_paths = None
            finally:
                _is_fx_tracing_flag = old_is_fx_tracing_flag
            return self.graph

        def __deepcopy__(self, memo):
            # _autowrap_search contains modules, which cannot be deepcopied.
            new_tracer = Tracer.__new__(Tracer)

            for k, v in self.__dict__.items():
                if k in {'_autowrap_search'}:
                    new_obj = copy.copy(v)
                else:
                    new_obj = copy.deepcopy(v, memo)

                new_tracer.__dict__[k] = new_obj

            return new_tracer

    # Dictionary of (id(globals dict), function name) => globals_dict to patch for
    # the purposes of the wrap() API.
    # We key by the globals dict id and function name to ensure we're wrapping a given
    # function only once.
    _wrapped_fns_to_patch: Dict[Tuple[int, str], dict] = {}

    # List of methods on classes to wrap (class type, function name)
    # this currently only works for Tensor.* methods that aren't traced properly
    _wrapped_methods_to_patch: List[Tuple[type, str]] = []

    if os.environ.get("FX_PATCH_GETITEM") == "1":
        # This change is needed to trace models like PositionalEmbedding from BERT:
        # https://github.com/pytorch/benchmark/blob/master/torchbenchmark/models/BERT_pytorch/bert_pytorch/model/embedding/position.py
        # but causes issues in quantization documented here:
        # https://github.com/pytorch/pytorch/issues/50710
        # once that is fixed we can make this the default behavior.
        _wrapped_methods_to_patch.append((torch.Tensor, "__getitem__"))

    def _find_proxy(*objects_to_search):
        """
        Recursively search a data structure for a Proxy() and return it,
        return None if not found.
        """
        proxy = None

        def find_proxy(x):
            nonlocal proxy
            if isinstance(x, Proxy):
                proxy = x

        map_aggregate(objects_to_search, find_proxy)
        return proxy

    def _create_wrapped_func(orig_fn):
        @functools.wraps(orig_fn)
        def wrapped(*args, **kwargs):
            """
            Given an closed-over ``orig_function`` to invoke, search the args and kwargs for
            a Proxy object. If there is one, emit a ``call_function`` node to preserve the
            call to this leaf function directly. Otherwise, just return the results of
            this function call, as this function is not being traced.
            """
            proxy = _find_proxy(args, kwargs)
            if proxy is not None:
                return_proxy = proxy.tracer.create_proxy(
                    "call_function", orig_fn, args, kwargs
                )
                return_proxy.node.meta["is_wrapped"] = True
                return return_proxy
            return orig_fn(*args, **kwargs)

        return wrapped

    def _create_wrapped_method(cls, name):
        orig_fn = getattr(cls, name)

        @functools.wraps(orig_fn)
        def wrapped(*args, **kwargs):
            """
            Search the args and kwargs for a Proxy object. If there is one,
            emit a ``call_method`` node to preserve the call to this method
            directly. Otherwise, just return the results of this function
            call, as this function is not being traced.
            """
            proxy = _find_proxy(args, kwargs)
            if proxy is not None:
                return proxy.tracer.create_proxy("call_method", name, args, kwargs)
            return orig_fn(*args, **kwargs)

        return wrapped

    class _PatchedFn(NamedTuple):
        frame_dict: Any
        fn_name: str
        orig_fn: Any

        def revert(self):
            raise NotImplementedError()

    class _PatchedFnSetItem(_PatchedFn):
        def revert(self):
            self.frame_dict[self.fn_name] = self.orig_fn

    class _PatchedFnDel(_PatchedFn):
        def revert(self):
            del self.frame_dict[self.fn_name]

    class _PatchedFnSetAttr(_PatchedFn):
        def revert(self):
            setattr(self.frame_dict, self.fn_name, self.orig_fn)

    class _Patcher:
        def __init__(self):
            super().__init__()
            self.patches_made: List[_PatchedFn] = []
            self.visited: Set[int] = set()

        def patch(
            self,
            frame_dict: Dict[str, Any],
            name: str,
            new_fn: Callable,
            deduplicate: bool = True,
        ):
            """
            Replace frame_dict[name] with new_fn until we exit the context manager.
            """
            new_fn.__fx_already_patched = deduplicate  # type: ignore[attr-defined]
            if name not in frame_dict and hasattr(builtins, name):
                self.patches_made.append(_PatchedFnDel(frame_dict, name, None))
            elif getattr(frame_dict[name], "__fx_already_patched", False):
                return  # already patched, no need to do it again
            else:
                self.patches_made.append(
                    _PatchedFnSetItem(frame_dict, name, frame_dict[name])
                )
            frame_dict[name] = new_fn

        def patch_method(
            self, cls: type, name: str, new_fn: Callable, deduplicate: bool = True
        ):
            """
            Replace object_or_dict.name with new_fn until we exit the context manager.
            """
            new_fn.__fx_already_patched = deduplicate  # type: ignore[attr-defined]
            orig_fn = getattr(cls, name)
            if getattr(orig_fn, "__fx_already_patched", False):
                return  # already patched, no need to do it again
            self.patches_made.append(_PatchedFnSetAttr(cls, name, orig_fn))
            setattr(cls, name, new_fn)

        def visit_once(self, thing: Any):
            """Return True on the first call to with thing, otherwise false"""
            idx = id(thing)
            if idx in self.visited:
                return False
            self.visited.add(idx)
            return True

        def __enter__(self):
            return self

        def __exit__(self, exc_type, exc_val, exc_tb):
            """
            Undo all the changes made via self.patch() and self.patch_method()
            """
            while self.patches_made:
                # unpatch in reverse order to handle duplicates correctly
                self.patches_made.pop().revert()
            self.visited.clear()

    def _patch_wrapped_functions(patcher: _Patcher):
        """
        Go through ``_wrapped_fn_patch_table`` and, for each frame object, wrap
        the listed global functions in the `_create_wrapped_func` wrapper.
        """
        for (_, name), frame_dict in _wrapped_fns_to_patch.items():
            if name not in frame_dict and hasattr(builtins, name):
                orig_fn = getattr(builtins, name)
            else:
                orig_fn = frame_dict[name]
            patcher.patch(frame_dict, name, _create_wrapped_func(orig_fn))

        for cls, name in _wrapped_methods_to_patch:
            patcher.patch_method(cls, name, _create_wrapped_method(cls, name))

    def _autowrap_check(
        patcher: _Patcher, frame_dict: Dict[str, Any], function_ids: Set[int]
    ):
        """
        Some methods, like `math.sqrt` are common enough we want to automatically wrap them as we see them.
        This method searches a scope for them and patches them if found.
        """
        if patcher.visit_once(frame_dict):
            for name, value in frame_dict.items():
                if (
                    not name.startswith("_")
                    and callable(value)
                    and id(value) in function_ids
                ):
                    patcher.patch(frame_dict, name, _create_wrapped_func(value))

    @compatibility(is_backward_compatible=True)
    def wrap(fn_or_name: Union[str, Callable]):
        """
        This function can be called at module-level scope to register fn_or_name as a "leaf function".
        A "leaf function" will be preserved as a CallFunction node in the FX trace instead of being
        traced through::

            # foo/bar/baz.py
            def my_custom_function(x, y):
                return x * x + y * y

            torch.fx.wrap('my_custom_function')

            def fn_to_be_traced(x, y):
                # When symbolic tracing, the below call to my_custom_function will be inserted into
                # the graph rather than tracing it.
                return my_custom_function(x, y)

        This function can also equivalently be used as a decorator::

            # foo/bar/baz.py
            @torch.fx.wrap
            def my_custom_function(x, y):
                return x * x + y * y

        A wrapped function can be thought of a "leaf function", analogous to the concept of
        "leaf modules", that is, they are functions that are left as calls in the FX trace
        rather than traced through.

        Args:

            fn_or_name (Union[str, Callable]): The function or name of the global function to insert into the
                graph when it's called
        """
        if not callable(fn_or_name) and not isinstance(fn_or_name, str):
            raise RuntimeError(
                "Unsupported type for global function! Must be either a callable or "
                "string name"
            )

        if callable(fn_or_name):
            assert not isinstance(fn_or_name, str)  # to make mypy happy
            fn_name = fn_or_name.__name__
        else:
            assert isinstance(
                fn_or_name, str
            ), "fn_or_name must be a global function or string name"
            fn_name = fn_or_name

        currentframe = inspect.currentframe()
        assert currentframe is not None
        f = currentframe.f_back
        assert f is not None
        if f.f_code.co_name != "<module>":
            raise NotImplementedError("wrap must be called at the top level of a module")

        # consider implementing Callable version of this via _autowrap_function_ids / _autowrap_search
        # semantics would be slightly different, but would add support `from x import wrapped_function`
        _wrapped_fns_to_patch[(id(f.f_globals), fn_name)] = f.f_globals
        return fn_or_name

    @compatibility(is_backward_compatible=True)
    def symbolic_trace(
        root: Union[torch.nn.Module, Callable[..., Any]],
        concrete_args: Optional[Dict[str, Any]] = None,
    ) -> GraphModule:
        """
        Symbolic tracing API

        Given an ``nn.Module`` or function instance ``root``, this function will return a ``GraphModule``
        constructed by recording operations seen while tracing through ``root``.

        ``concrete_args`` allows you to partially specialize your function, whether it's to remove control flow or data structures.

        For example::

            def f(a, b):
                if b == True:
                    return a
                else:
                    return a*2

        FX can typically not trace through this due to the presence of control
        flow. However, we can use `concrete_args` to specialize on the value of
        `b` to trace through this::

            f = fx.symbolic_trace(f, concrete_args={'b': False})
            assert f(3, False)  == 6

        Note that although you can still pass in different values of `b`, they will be ignored.

        We can also use `concrete_args` to eliminate data-structure handling from
        our function. This will use pytrees to flatten your input. To avoid
        overspecializing, pass in `fx.PH` for values that shouldn't be
        specialized. For example::

            def f(x):
                out = 0
                for v in x.values():
                    out += v
                return out
            f = fx.symbolic_trace(f, concrete_args={'x': {'a': fx.PH, 'b': fx.PH, 'c': fx.PH}})
            assert f({'a': 1, 'b': 2, 'c': 4}) == 7

        Args:
            root (Union[torch.nn.Module, Callable]): Module or function to be traced and converted
                into a Graph representation.
            concrete_args (Optional[Dict[str, any]]): Inputs to be partially specialized

        Returns:
            GraphModule: a Module created from the recorded operations from ``root``.
        """
        tracer = Tracer()
        graph = tracer.trace(root, concrete_args)
        name = (
            root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__
        )
        return GraphModule(tracer.root, graph, name)

    @wrap
    def _assert_is_none(value, msg):
        assert value is None, msg

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