code: def run_functionalized_fw_and_collect_metadata(
      f,
      *,
      keep_input_mutations: bool,
      # TODO: refactor to kill this flag
      is_train: bool = False,
      requires_subclass_dispatch: bool = False,
      pre_dispatch: bool = False,
  ) -> Callable[..., ViewAndMutationMeta]:
      memo: Dict[Tensor, Tensor] = {}
  
      def _to_fun(t):
          if isinstance(t, Tensor):
              if t in memo:
                  return memo[t]
              r = to_fun(t)
              memo[t] = r
              return r
          else:
              return t
  
      @wraps(f)
      def inner(*flat_args):
          # This function is meant to be run with the forward, which expects a flat list of tensor/symint/other args.
          assert all(isinstance(a, KNOWN_TYPES) for a in flat_args)
  
          input_info: List[InputAliasInfo] = []
          output_info: List[OutputAliasInfo] = []
  
          prior_grad_enabled = torch.is_grad_enabled()
          prior_autocast_states = _get_autocast_states()
  
          # See Note [Disabling Functionalize TLS Above Python Functionalization]
          disable_above = torch._C._ExcludeDispatchKeyGuard(
              torch._C.DispatchKeySet(
                  torch._C.DispatchKey.Functionalize
              )
          )
  
          # It doesn't matter if we run this under predispatch or not because it is
          # only for figuring out metadata
          with disable_above, FunctionalTensorMode():
              # precondition: The passed in function already handles unflattening inputs + flattening outputs
              flat_f_args = pytree.tree_map(_to_fun, flat_args)
              flat_f_outs = f(*flat_f_args)
  
          if prior_autocast_states != _get_autocast_states():
              raise RuntimeError(
                  "AOTAutograd does not support tracing graphs that mutate the autocast state. "
                  "Dynamo will only insert autocast context managers (e.g. with torch.autocast(..)) into the graph, "
                  "which will unwind all of their mutations to autocast state before the graph exits. "
                  "If you encounter this error while using torch.compile, please file a bug."
              )
  
          # Inspect the state of the input tensor functional wrapper to detect input mutation info
          # If inp[i] has a metadata-only mutation, then maybe_inputs_with_mutated_metadata[i] contains the updated version
          for i, (arg, f_arg) in enumerate(zip(flat_args, flat_f_args)):
              if not isinstance(arg, Tensor):
                  new_arg = arg
              else:
                  new_arg = from_fun(f_arg)
              mutates_metadata = has_metadata_mutation(
                  f_arg, arg, check_only_storage_mutation=False
              )
              if mutates_metadata and is_traceable_wrapper_subclass(
                  arg
              ):
                  raise RuntimeError(
                      "Metadata mutations are currently not allowed on tensor subclasses"
                  )
              mutates_storage_metadata = has_metadata_mutation(
                  f_arg, arg, check_only_storage_mutation=True
              )
              mutates_data = has_data_mutation(f_arg)
              mutations_hidden_from_autograd = (
                  are_all_mutations_hidden_from_autograd(f_arg)
              )
              mutations_under_no_grad_or_inference_mode = (
                  mutates_data
                  and are_all_mutations_under_no_grad_or_inference_mode(
                      f_arg
                  )
              )
  
              # Here, we're saying that if an input experienced a set call, inp.set_(other),
              # then we can effectively not have to worry about whether its data was mutated.
              # There are 3 cases:
              # (1) We mutate inp *after* the set_() call. other is a graph intermediate.
              #     In this case, we're not really mutating the input storage of "inp";
              #     we're mutating the storage of an intermdiate value (other),
              #     and slamming that storage into the input tensor. So no data mutation is necessary.
              # (2) We mutate inp *after* the set_() call. other is a graph *input*.
              #     In this case, the data mutation will be properly handled in the runtime
              #     epilogue during the processing of "other"
              # (3) We mutate inp *before* the set_() call.
              #     This case is *not* currently handled.
              #     TODO: discuss this in the PR. Both supporting this, and detecting + erroring out,
              #     seem painful to get working.
              if mutates_storage_metadata:
                  mutates_data = False
  
              requires_grad = (
                  isinstance(f_arg, torch.Tensor)
                  and f_arg.requires_grad
              )
  
              input_info.append(
                  InputAliasInfo(
                      is_leaf=isinstance(arg, Tensor)
                      and safe_is_leaf(arg),
                      mutates_data=mutates_data,
                      mutates_metadata=mutates_metadata,
                      mutations_hidden_from_autograd=mutations_hidden_from_autograd,
                      mutates_storage_metadata=mutates_storage_metadata,
                      mutations_under_no_grad_or_inference_mode=mutations_under_no_grad_or_inference_mode,
                      requires_grad=requires_grad,
                      mutation_type=_get_mutation_type(
                          keep_input_mutations,
                          mutates_data,
                          mutates_metadata,
                          mutations_hidden_from_autograd,
                          mutations_under_no_grad_or_inference_mode,
                          requires_grad,
                      ),
                  )
              )
  
          # If a function involves creating a tensor, and returning a view of it, such that its _base is the intermediate,
          # We need to make sure our graph returns the _base as a graph output, and we manually recreate the view
          # to return to the user. Why? The backend compiler is free to (incorrectly) not set requires_grad
          # on the base tensor, but we are obligated to properly set requires-gradness on the real output.
  
          inp_storage_refs = {
              StorageWeakRef(inpt.untyped_storage()): idx
              for idx, inpt in enumerate(flat_f_args)
              if isinstance(inpt, Tensor)
          }
  
          # We need inp tensor id's to be able to tell if an outputs **are** inputs.
          inp_tensor_ids = {
              id(inpt)
              for inpt in flat_f_args
              if isinstance(inpt, Tensor)
          }
          # We need output tensor id's to tell if any output._base` attributes **are** other outputs.
          # (This is also a dict because we need to know that output's index, so we can regenerate
          # the alias from it).
          out_tensor_ids = {id(o): i for i, o in enumerate(flat_f_outs)}
  
          # Keep track of which outputs alias other outputs
          out_tensor_alias_counts: DefaultDict = (
              collections.defaultdict(int)
          )
          # This tells us, for a given group of outputs that alias each other,
          # whether they e.g. all came from an unbind call
          num_aliased_tensors_that_are_multi_output_views: DefaultDict = collections.defaultdict(
              int
          )
          out_storage_to_tensors: DefaultDict = collections.defaultdict(
              set
          )
          curr_storage = None
          for o in flat_f_outs:
              if isinstance(o, torch.Tensor):
                  curr_storage = StorageWeakRef(o.untyped_storage())
                  out_tensor_alias_counts[curr_storage] += 1
                  # Note: [AOTAutograd: differentiable outputs that alias each other from a multi-output view call]
                  # This is an optimization on top of the "alias of intermediates" logic,
                  # which you can read more about under Note [AOT Autograd: outputs aliasing inputs or intermediates!]
                  #
                  # Before describing the optimization: this is important for AOTAutograd to have good
                  # perf around, multi-output views. HOWEVER:
                  # - There is a more generic change to AOTAutograd that we'd like to make, that subsumes this case,
                  #   around using pre-dispatch tracing to partition out a graph so we can faithfully replay all
                  #   views without having to regenerate them at runtime.
                  # - It's loosely described in this doc (more details will be added soon):
                  #   https://docs.google.com/document/d/1DlfFq8TKbuAn2zyJxLfoW-X1qkkm5PLdHFtySo03QAk/edit
                  # - Once that change lands, we should just rip out this "optimization", since:
                  #   (1) It will be fully unnecessary
                  #   (2) Although it is only a few lines of code, it is a bit difficult to reason about
                  #       its correctness with the autograd engine in all cases.
                  #
                  #
                  # What is this optimization? Consider the below case:
                  # def f(x):
                  #     intermediate = x.mul(2)
                  #     # x and intermediate here require grad
                  #     o1, o2, ... o10 = intermediate.unbind(-1)
                  #     return intermediate, o1, o2, ... o10
                  # Now, the "intermediate base" handling in AOTAutograd implies that we must do the following:
                  #   (1) return "intermediate as an extra output of the compiled graph
                  #   (2) regenerate each aliased output off of "intermediate", **outside** of the autograd.Function.
                  # The reason AOTAutograd ordinarily does this is for safety: the autograd engine needs to know
                  # that o1 through o10 are all aliased, and if we blindly return o1 through o10 from the autograd.Function,
                  # this information will be hidden.
                  # In particular, mutating one alias might require autograd to update autograd metadata on the other aliases
                  # (like their grad_fn, for example, when the autograd engine needs to do view-replay).
                  #
                  # However, intermediate_base logic can be bad for backward performance (we sometimes generate
                  # as_strided calls during the intermediate base logic, which can have a slow backward formula).
                  # Is it possible to find a set of conditions where it is **safe** to hide the output aliasing from autograd?
                  #
                  # For a set of outputs of the graph that alias each other, o_1...o_k, consider:
                  # (1) They came from the same multi-output view op, e.g. o_1, ..., o_k = intermediate.unbind(0)
                  # (2) If there are any other aliases of o_1 through o_k (in the example above, intermediate),
                  #     **at most** 1 can escape from the graph (e.g. there is not some other graph input/output
                  #     o_other, that aliases these outputs)
                  # (3) o_1...o_k all require_grad, they all share the same ._base, and their ._base requires grad.
                  #     This condition is important because it's what causes slowness in the intermediate_base
                  #     codepath of aot_autograd. Ordinarily, o_1...o_k would all get a grad_fn, and
                  #     aot_autograd's view-replay might give each output an AsStridedBackward as its grad_fn.
                  #     "K" AsStridedBackward calls will be *much* slower than a single UnbindBackward.
                  # In this setup, is it possible to mutate one of the outputs o_i in a way that would affect the autograd meta
                  # of the other aliases?
                  #
                  # Claim: No! Consider a few example (which I'm pretty sure cover all cases of mutation w.r.t. autograd):
                  # (a) What happens if we mutate any of o_1 through o_k directly?
                  #     Autograd raises an error:
                  #     "RuntimeError: Output 0 of UnbindBackward0 is a view and is being modified inplace. This view is
                  #      the output of a function that returns multiple views. Such functions do not allow the output
                  #      views to be modified inplace. You should replace the inplace operation by an out-of-place one."
                  # (b) What if we take a view of o_k and mutate it, o_k.view(o_k.shape).mul_(2)?
                  #     Autograd raises the same error- the "multi-output-view"ness of an alias propagates to future views.
                  # (c) What if we mutate o_k under no_grad?
                  #     Autograd raises the same error
                  # (d) What if we detach and mutate, e.g. o_k.detach().mul_(2)?
                  #     Autograd allows this, *but* autograd updates all alias's grad_fn's to be error functions when accessed.
                  #     Autograd raises the same error
                  # (e) What if we try to mutate another alias of o_1...o_k, that was **not** created from a multi-output view?
                  #     We promised that there is at most **one** such alias, e.g. intermediate in the example above.
                  #     You can mutate intermediate, but in eager mode this will change the grad_fn of o_1...o_k
                  #     to be error fn's.
                  #     Since intermediate was the *only* non-multi-output-alias, there are no other aliases
                  #     of `intermediate` around that were produced by the compiled fn and have a valid grad_fn.
                  #
                  # Coming back to this optimization:
                  # Given that it is not possible for mutating one of these aliases to affect the autograd metadata of another alias
                  # without causing an error in eager mode, we will simple hide the aliasing from autograd during torch.compile
                  # if all of the above conditions are met.
                  # This has the slight downside that it's possible to write some "bad" code that autograd will raise an error on
                  # in eager but fail to during torch.compile, but it has the benefit that this code has much better performance.
                  # NOTE: if and when we eventually update AOTAutograd to do the "view graph slicing" defined here:
                  # https://docs.google.com/document/d/1DlfFq8TKbuAn2zyJxLfoW-X1qkkm5PLdHFtySo03QAk/edit,
                  # then this optimization will probably matter less and might be ok to remove.
                  is_cur_tensor_multi_out_view = isinstance(
                      o, FunctionalTensor
                  ) and torch._functionalize_is_multi_output_view(  # type: ignore[attr-defined]
                      o.elem
                  )
                  if is_cur_tensor_multi_out_view:
                      num_aliased_tensors_that_are_multi_output_views[
                          curr_storage
                      ] += 1
                  out_storage_to_tensors[curr_storage].add(o)
  
          # maps the id of an intermediate base to its index in the output of the compiled forward
          intermediate_base_tensor_id_to_output_idx: Dict[int, int] = {}
          intermediate_bases: List[torch.Tensor] = []
          # Why Do We Care If Storage Changed?
          # It's important to understand the implications of storage changes in complex scenarios. Take this example:
          #
          # def f(x):
          #     x_storage = x.untyped_storage()
          #     non_leaf_tensor = torch.ones(4, requires_grad=True).clone()
          #
          #     # Using no_grad() and _unsafe_preserve_version_counter to simulate the .data = operation
          #     with torch.no_grad(), torch.autograd._unsafe_preserve_version_counter(x):
          #         x.set_(non_leaf_tensor.untyped_storage())
          #
          #     out = x.view(-1)
          #
          #     # Restoring x to its original storage, again simulating .data = operation
          #     with torch.no_grad(), torch.autograd._unsafe_preserve_version_counter(x):
          #         x.set_(x_storage)
          #
          #     return out
          #
          # In this scenario, 'x' and 'out' have different shapes and are stored at different memory addresses, aka no aliasing.
          # However, due to how set_() and more specificlaly, set is functionalized, is defined to preserve eager semantics,
          # the autograd engine mistakenly assumes that 'x' and 'out' are aliased, treating 'x' as 'out._base'.
          # This misinterpretation leads to an 'alias_of_input' flag, causing an unnecessary as_strided() call to be generated,
          # which could lead to issues later in the code.
          for o in flat_f_outs:
              functional_tensor_storage_changed = isinstance(
                  o, FunctionalTensor
              ) and torch._functionalize_was_storage_changed(  # type: ignore[attr-defined]
                  o.elem
              )
              curr_storage = (
                  None
                  if not isinstance(o, torch.Tensor)
                  else StorageWeakRef(o.untyped_storage())
              )
              outs_with_identical_metadata_that_require_grad = (
                  []
                  if not isinstance(o, Tensor)
                  else [
                      curr
                      for curr in out_storage_to_tensors[curr_storage]
                      if has_same_metadata(o, curr)
                      and curr.requires_grad
                      and o is not curr
                  ]
              )
  
              # See Note [Accessing .grad_fn on FunctionalTensor]
              # In-place operations on views will trigger a lazy rebase of the autograd graph;
              # this runs during access to the .grad_fn. The rebase logic will invoke view ops
              # on FunctionalTensors, so we must enable a FunctionalTensorMode here to ensure
              # these op calls succeed.
              grad_fn = None
              if isinstance(o, Tensor):
                  with FunctionalTensorMode():
                      grad_fn = o.grad_fn
  
              is_result_of_custom_autograd_fn = False
              # Need to check for both custom cpp (CppFunction) and python (BackwardCFunction)
              # autograd fns
              if type(grad_fn).__name__ == "CppFunction":
                  is_result_of_custom_autograd_fn = True
              if isinstance(
                  grad_fn, torch.autograd.function.BackwardCFunction
              ):
                  is_result_of_custom_autograd_fn = True
  
              if not isinstance(o, Tensor):
                  output_type = OutputType.non_alias
                  base_idx = None
              elif (
                  curr_storage in inp_storage_refs
                  and grad_fn is not None
                  and is_result_of_custom_autograd_fn
              ):
                  output_type = OutputType.custom_function_view
                  base_idx = None
              elif (
                  curr_storage in inp_storage_refs
                  and not functional_tensor_storage_changed
              ):
                  base_idx = inp_storage_refs[curr_storage]
                  is_input_tensor = id(o) in inp_tensor_ids
                  num_aliased_outs = out_tensor_alias_counts[
                      curr_storage
                  ]
                  num_multi_output_view_outs = (
                      num_aliased_tensors_that_are_multi_output_views[
                          curr_storage
                      ]
                  )
                  num_aliased_outs_that_are_not_multi_output_views = (
                      num_aliased_outs - num_multi_output_view_outs
                  )
                  if (
                      grad_fn is not None
                      and num_aliased_outs_that_are_not_multi_output_views
                      == 0
                  ):
                      # See Note: [AOTAutograd: differentiable outputs that alias each other from a multi-output view call]
                      # In particular, given:
                      # def f(x):
                      #     return list(x.unbind(0))
                      # The main reason we ordinarily try to regenerate these output aliases outside of the
                      # compiled autograd.Function is because if any of the outputs are later mutated,
                      # autograd needs to perform view-replay to regenerate them.
                      # However, autograd does not allow users to mutate multi-output views
                      # in any way that can change the autograd metadata of other aliases.
                      # So we hide this aliasing from autograd here.
                      aot_graphs_log.info(
                          "Encountered AOTAutograd case: differentiable outputs that \
  alias each other from a multi-output view call"
                      )
                      output_type = OutputType.non_alias
                  elif is_input_tensor:
                      output_type = OutputType.is_input
                  else:
                      output_type = OutputType.alias_of_input
  
              # We only need to handle the intermediate base case when both
              # the intermediate base and the output require gradients.
              # See Note [AOT Autograd: outputs aliasing inputs or intermediates!]
              elif (
                  o._base is not None
                  and o.requires_grad
                  and o._base.requires_grad
              ):
                  num_aliased_outs = out_tensor_alias_counts[
                      curr_storage
                  ]
                  num_multi_output_view_outs = (
                      num_aliased_tensors_that_are_multi_output_views[
                          curr_storage
                      ]
                  )
                  num_aliased_outs_that_are_not_multi_output_views = (
                      num_aliased_outs - num_multi_output_view_outs
                  )
                  # Note: [AOTAutograd: differentiable outputs that alias each other from a multi-output view call]
                  if (
                      out_tensor_alias_counts[curr_storage] == 1
                      or num_aliased_outs_that_are_not_multi_output_views
                      <= 1
                  ):
                      # Note [Intermediate Bases Optimization]
                      # Normally if we have an output that aliases an intermediate,
                      # we need to add the extra "intermediate base" logic further down
                      # to prevent autograd from yelling at us if the user later tries to
                      # mutate that output.
                      # However, the common case here is if we have an output that aliases an intermediate,
                      # but doesn't alias any other outputs.
                      # In that case, autograd shouldn't have to worry about the aliasing at all
                      # (if that output is mutated, there are no other live aliases for autograd to worry about).
                      # The "intermediate bases" can hurt inductor perf by forcing more variables to become outputs.
                      # So as an optimization, we won't do intermediate base handling in this case.
                      # Instead, we'll hide the aliasing from autograd using aten._unsafe_view().
                      if (
                          out_tensor_alias_counts[curr_storage] != 1
                          and num_aliased_outs_that_are_not_multi_output_views
                          <= 1
                      ):
                          aot_graphs_log.info(
                              "Encountered AOTAutograd case: differentiable outputs that alias each other \
  from a multi-output view call"
                          )
                      output_type = OutputType.unsafe_view_alias
                      base_idx = None
                  else:
                      # First, check if o's ._base is an existing output
                      maybe_existing_out_idx = out_tensor_ids.get(
                          id(o._base), None
                      )
                      if maybe_existing_out_idx is not None:
                          # Special case where the output is an alias of a graph intermediate, but that intermediate
                          # is itself also a user output.
                          output_type = (
                              OutputType.alias_of_intermediate_base_is_user_output
                          )
                          base_idx = maybe_existing_out_idx
                      else:
                          # Next, check if o's ._base is an intermediate base that we already returned
                          maybe_existing_base_output_idx = intermediate_base_tensor_id_to_output_idx.get(
                              id(o._base), None
                          )
                          if maybe_existing_base_output_idx is not None:
                              output_type = (
                                  OutputType.alias_of_intermediate
                              )
                              base_idx = maybe_existing_base_output_idx
                          else:
                              # Otherwise, take o._base and explicitly return it as an output in the compiled graph
                              new_out_idx = len(intermediate_bases)
                              base_idx = new_out_idx
                              # Indicate to the logic later on (when we trace the joint)
                              # that this particular output should get it's ._base appended to the forward graph outputs
                              output_type = (
                                  OutputType.alias_of_intermediate_save_as_output
                              )
                              intermediate_base_tensor_id_to_output_idx[
                                  id(o._base)
                              ] = new_out_idx
                              intermediate_bases.append(o._base)
              elif (
                  # See https://github.com/pytorch/pytorch/issues/100348 for this case.
                  # This protects against the specific case where a user fn returns (output, output.detach())
                  out_tensor_alias_counts[curr_storage] > 1
                  and len(
                      outs_with_identical_metadata_that_require_grad
                  )
                  > 0
                  and not o.requires_grad
              ):
                  assert (
                      len(
                          outs_with_identical_metadata_that_require_grad
                      )
                      > 0
                  )
                  # In theory we could use any of these tensors to regenerate the aliased outputs from,
                  # since they all alias each other and have identical metatadata
                  out_alias = (
                      outs_with_identical_metadata_that_require_grad[0]
                  )
                  existing_out_idx = out_tensor_ids[id(out_alias)]
                  output_type = (
                      OutputType.alias_of_intermediate_base_is_user_output
                  )
                  base_idx = existing_out_idx
              else:
                  output_type = OutputType.non_alias
                  base_idx = None
  
              if isinstance(o, torch.Tensor):
                  dynamic_dims = {
                      i
                      for i, s in enumerate(o.shape)
                      if not is_concrete_int(s)
                  }
              else:
                  dynamic_dims = None
              out_info = OutputAliasInfo(
                  output_type=output_type,
                  raw_type=type(o),
                  base_idx=base_idx,
                  dynamic_dims=dynamic_dims,
                  requires_grad=isinstance(o, torch.Tensor)
                  and o.requires_grad,
              )
              output_info.append(out_info)
  
          # See Note [AOT Autograd: Views to avoid tangents aliasing inputs]
          def view_avoid_dupes_with_primals(t):
              if isinstance(
                  t, Tensor
              ) and is_traceable_wrapper_subclass(t):
                  return transform_subclass(
                      t,
                      lambda _, inner_t: view_avoid_dupes_with_primals(
                          inner_t
                      ),
                  )
              if isinstance(t, Tensor):
                  return t.view(t.shape)
              return t
  
          # This analysis function returns *only* the outputs that are meant to be tangents to the backwards.
          # Anything that aliases (inputs returned in the fw due to metadata mutations, or outputs that alias inputs/intermediates)
          # are *regenerated* later, and not used directly in the autograd graph
          f_input_tangents = [
              inp
              for inp, info in zip(flat_f_args, input_info)
              if _get_mutation_type(
                  keep_input_mutations,
                  mutates_data=info.mutates_data,
                  mutates_metadata=info.mutates_metadata,
                  mutations_hidden_from_autograd=info.mutations_hidden_from_autograd,
                  mutations_under_no_grad_or_inference_mode=info.mutations_under_no_grad_or_inference_mode,
                  requires_grad=info.requires_grad
                  # MUTATED_OUT_GRAPH corresponds to any input mutations that happen outside the graph.
                  # this can also include metadata mutations, and inputs that do not require grad,
              )
              == MutationType.MUTATED_OUT_GRAPH
              and info.mutates_data
              and info.requires_grad
          ]
          f_output_tangents = [
              o
              for o, info in zip(flat_f_outs, output_info)
              if info.output_type
              in [
                  OutputType.non_alias,
                  OutputType.unsafe_view_alias,
                  OutputType.custom_function_view,
              ]
              and issubclass(info.raw_type, torch.Tensor)
              and info.requires_grad
          ]
          # intermediate bases are also included in the backward graph
          f_tangents = (
              f_input_tangents + f_output_tangents + intermediate_bases
          )
          traced_tangents = pytree.tree_map(from_fun, f_tangents)
          traced_tangents = pytree.tree_map(
              view_avoid_dupes_with_primals, traced_tangents
          )
          user_outs = pytree.tree_map(from_fun, f_output_tangents)
  
          f_mutated_inputs = [
              inp
              for inp, info in zip(flat_f_args, input_info)
              if info.mutates_data or info.mutates_metadata
          ]
          f_metadata_mutated_inputs = [
              inp
              for inp, info in zip(flat_f_args, input_info)
              if info.mutates_metadata
          ]
          # This logic (annoyingly) re-figures out exactly what the outputs to the compiled fw graph will be.
          # When handling subclasses, we need info about **all** outputs of compiled forward graph,
          # so we know precisely which graph outputs to wrap back into tensor subclasses
          # Ideally we would refactor this so not have an is_train flag, and have the separate
          # inference and training paths decide which inputs/output to ask for subclass info on.
          # However, we currently stash indexing information on each SubclassMeta about its order
          # in the graph outputs list.
          f_fw_graph_outs = list(flat_f_outs)
          if is_train or not keep_input_mutations:
              f_fw_graph_outs = f_mutated_inputs + f_fw_graph_outs
          else:
              # even when "keep_input_mutations" is True,
              # we never keep metadata-only mutations in the fw graph
              f_fw_graph_outs = (
                  f_metadata_mutated_inputs + f_fw_graph_outs
              )
          if is_train:
              f_fw_graph_outs = f_fw_graph_outs + intermediate_bases
          fw_graph_outs = pytree.tree_map(from_fun, f_fw_graph_outs)
  
          grad_enabled_mutation = None
          if torch.is_grad_enabled() != prior_grad_enabled:
              grad_enabled_mutation = torch.is_grad_enabled()
              torch.set_grad_enabled(
                  prior_grad_enabled
              )  # Restore the prior state after tracing it
              aot_graphs_log.info(
                  (
                      "grad_mode mutation encountered in graph. "
                      "Will emit mutation epilogue, to set grad_mode=%s"
                  ),
                  grad_enabled_mutation,
              )
  
          metadata = ViewAndMutationMeta(
              input_info=input_info,
              output_info=output_info,
              num_intermediate_bases=len(intermediate_bases),
              keep_input_mutations=keep_input_mutations,
              traced_tangents=traced_tangents,
              subclass_inp_meta=create_subclass_meta(flat_args),
              subclass_fw_graph_out_meta=create_subclass_meta(
                  fw_graph_outs
              ),
              subclass_tangent_meta=create_subclass_meta(
                  traced_tangents
              ),
              is_train=is_train,
              grad_enabled_mutation=grad_enabled_mutation,
              requires_subclass_dispatch=requires_subclass_dispatch,
          )
          return metadata
  
      return inner
  
file_path: pytorch/torch/_functorch/_aot_autograd/collect_metadata_analysis.py
imports: import collections
  from functools import wraps
  from typing import Callable, DefaultDict, Dict, List
  import torch
  import torch.utils._pytree
  from torch import Tensor
  from torch._logging import getArtifactLogger
  from torch._subclasses.functional_tensor import FunctionalTensor, FunctionalTensorMode
  from torch._subclasses.meta_utils import safe_is_leaf
  from torch.fx.experimental.symbolic_shapes import is_concrete_int
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._python_dispatch import is_traceable_wrapper_subclass, transform_subclass
  from .functional_utils import _get_mutation_type, are_all_mutations_hidden_from_autograd, are_all_mutations_under_no_grad_or_inference_mode, from_fun, has_data_mutation, has_metadata_mutation, has_same_metadata, to_fun
  from .schemas import InputAliasInfo, MutationType, OutputAliasInfo, OutputType, ViewAndMutationMeta
  from .subclass_utils import create_subclass_meta
  from .utils import _get_autocast_states, KNOWN_TYPES, strict_zip
label_desc: Use image node_id 1 for calling a global function with example usage: run_functionalized_fw_and_collect_metadata(f) and returns: inner, metadata, r, t, t, memo, transform_subclass, t