code: def aot_dispatch_autograd(
      flat_fn,
      flat_args: List[Any],
      aot_config: AOTConfig,
      *,
      fw_metadata: ViewAndMutationMeta,
  ):
      fw_metadata.deterministic = (
          torch.are_deterministic_algorithms_enabled()
      )
      fx_g, joint_inputs, maybe_subclass_meta = aot_dispatch_autograd_graph(  # type: ignore[misc]
          flat_fn, flat_args, aot_config, fw_metadata=fw_metadata
      )
  
      # Copied from aot_dispatch_autograd_graph.
      traced_tangents = pytree.tree_map(
          lambda x: x.detach().contiguous()
          if isinstance(x, Tensor)
          else x,
          fw_metadata.traced_tangents,
      )
      disable_amp = torch._C._is_any_autocast_enabled()
  
      if aot_config.enable_log:
          aot_joint_log.info(
              "%s",
              lazy_format_graph_code(
                  "Joint graph", fx_g, aot_config.aot_id
              ),
          )
  
      with torch.no_grad():
          inner_meta = (
              fw_metadata
              if maybe_subclass_meta is None
              else maybe_subclass_meta.fw_metadata
          )
          with track_graph_compiling(aot_config, "joint"):
              # See Note: [Partitioner handling for Subclasses, Part 1]
              num_inner_fwd_outputs = (
                  inner_meta.num_mutated_inp_runtime_indices
                  + inner_meta.num_outputs
                  + inner_meta.num_intermediate_bases
                  + inner_meta.num_outputs_rng_offset
              )
              fw_module, bw_module = aot_config.partition_fn(
                  fx_g,
                  joint_inputs,
                  num_fwd_outputs=num_inner_fwd_outputs,
              )
              fw_outs = next(
                  n for n in fw_module.graph.nodes if n.op == "output"
              ).args[0]
              # we only need to bookkeep the symints that are saved for bw, not any symints
              # the user forward might have returned in its own output
              fw_outs_saved_for_bw = fw_outs[num_inner_fwd_outputs:]
              num_fw_outs_saved_for_bw = len(fw_outs_saved_for_bw)
              symint_outs_saved_for_bw = [
                  n for n in fw_outs_saved_for_bw if is_sym_node(n)
              ]
              fw_metadata.num_symints_saved_for_bw = len(
                  symint_outs_saved_for_bw
              )
              inner_meta.num_symints_saved_for_bw = len(
                  symint_outs_saved_for_bw
              )
              _num_symints_saved_for_bw = len(symint_outs_saved_for_bw)
  
          # Note [Detaching inputs that never need gradients]
          # See https://github.com/pytorch/pytorch/issues/97745
          # Suppose we have a function like this that we want to compile:
          #
          # def f(x, y):
          #     return torch.mul(x, y.detach())
          #
          # What gradients should we compute for x and y?
          # By default, AOTAutograd will compute a gradient for **every** input that requires gradients,
          # and so we'll compute:
          #    x_grad_input = y
          #    y_grad_input = None
          # Does this preserve the semantics of eager mode?
          # Unfortunately, no.
          # Doing the above will cause autograd to **continue** to backprop the autograd tape
          # that was generated from constructing y.
          #
          # This is **different** from what would have happened in eager mode.
          # In eager mode, if we backprop through the output of this function, autograd will only traverse
          # the bit of the autograd tape corresponding to "x".
          # In particular, if a user had previously backpropped through y's autograd tape,
          # And then they try to backprop through the output of the above function,
          # then we'll hit the dreaded "Trying to backward through the graph a second time" error.
          #
          # You might think: If autograd sees that a gradient is None, shouldn't it stop early,
          # instead of continuing the backprop through the ancestors of that node in the graph?
          #
          # Autograd has two passes:
          # (1) a first pass that traverses the autograd graph and figures out which nodes need to be executed
          # (2) a second pass that actually goes ahead and executes each node when it becomes ready,
          #     propagating gradients
          # By the time we're executing a node and we see that it produces a None, the set of nodes to execute
          # is already locked-in.
          #
          # The fix: instead, we can recognize statically that the graph we're compiling will never contribute
          # gradients to y, and prevent autograd from trying to traverse y's autograd tape at all.
          # We can do this by manually detach'ing y before sending it through the `CompiledFunction`.
          #
          # Note that this solution is not bulletproof.
          # It's possible to construct a case where eager may or may not have have tried to autograd through y,
          # depending on the actual grad_outputs that were passed in during the backward.
          # There is no easy fix for this: the simplest fix would be to run with `retain_graph=True`,
          # allowing autograd to re-use the graph.
          #
          # An example of this case is:
          # def f(x):
          #     return x.detach() * 2, x * 3
          # If we were to only backprop through outs[0], in eager, we would stop
          # If we backward only on the first output, we shouldn't send a grad through x.
          # But the custom autograd function doesn't know that: it will materialize zero grads for x * 3
          # and we will end up with a zero grad at x.
          # If we later backprop through the second output, this will also require backprop'ing through x.
          # Meaning we'll need to use `retain_graph=True` to be able to backprop through x the second time.
          _indices_of_inps_to_detach = []
          bw_outs = next(
              n for n in bw_module.graph.nodes if n.op == "output"
          ).args[0]
  
          # TODO: we should apply the below "detach inputs if their gradients are statically known to be None"
          # optimization even if we have subclass inputs/outputs (we do not handle this today).
          # Computing which our our inputs get None gradients is a bit more complicated,
          # if any of our inputs are subclasses. Why?
          # (a) we need to make sure that we call .detach() on the input subclasses, since autograd sees subclasses.
          # (b) The grad_outputs that we AOT computed in our backward graph are the desugared tensor tensors,
          #     so we need to figure out which subclass fw inputs they map to.
          if maybe_subclass_meta is None:
              assert (
                  len(bw_outs)
                  == len(fw_metadata.input_info)
                  + inner_meta.num_outputs_rng_offset
              )
              for i, (bw_out) in enumerate(bw_outs):
                  if bw_out is None:
                      _indices_of_inps_to_detach.append(i)
  
          if aot_config.enable_log:
              aot_graphs_log.info(
                  "%s",
                  lazy_format_graph_code(
                      "Forward graph", fw_module, aot_config.aot_id
                  ),
              )
              aot_graphs_log.info(
                  "%s",
                  lazy_format_graph_code(
                      "Backward graph", bw_module, aot_config.aot_id
                  ),
              )
  
          with track_graph_compiling(aot_config, "forward"):
              # flat_args at this point might still be subclasses-
              # make sure to pass the unwrapped fake tensors into the compiler!
              adjusted_flat_args = joint_inputs[0]
              if config.functionalize_rng_ops:
                  # Update example inputs for the fw_compiler
                  fake_mode = detect_fake_mode()
                  (
                      seed,
                      offset,
                  ) = CUDARngStateHelper.get_torch_state_as_tuple(
                      fake_mode
                  )
                  adjusted_flat_args.extend([seed, offset])
                  # We are not clearing flat_args here because
                  # 1) There is a check in the debug compiler at the end
                  # 2) It does not matter as these are fake tensors
  
              if (
                  tracing_context := torch._guards.TracingContext.try_get()
              ):
                  tracing_context.fw_metadata = inner_meta
  
              with TracingContext.report_output_strides() as fwd_output_strides:
                  compiled_fw_func = aot_config.fw_compiler(
                      fw_module, adjusted_flat_args
                  )
              if not hasattr(compiled_fw_func, "_boxed_call"):
                  compiled_fw_func = make_boxed_func(compiled_fw_func)
  
              if maybe_subclass_meta is not None:
                  # Why do we need to pass in num_fw_outs_saved_for_bw?
                  # See Note: [Partitioner handling for Subclasses, Part 2]
                  compiled_fw_func = aot_dispatch_subclass_wrapper(
                      compiled_fw_func,
                      subclass_metas=fw_metadata.subclass_fw_graph_out_meta,
                      num_fw_outs_saved_for_bw=num_fw_outs_saved_for_bw,
                  )
                  if not hasattr(compiled_fw_func, "_boxed_call"):
                      compiled_fw_func = make_boxed_func(
                          compiled_fw_func
                      )
  
          # NB: It's important to compile backwards ahead of time, as this may
          # add extra guards which we need to apply to the Dynamo cache at
          # forwards
          with track_graph_compiling(aot_config, "backward"):
              placeholder_list = fx_placeholder_vals(bw_module)
  
              forward_saved_for_backwards_strides = None
              if fwd_output_strides is not None:
                  forward_saved_for_backwards_strides = (
                      fwd_output_strides[
                          inner_meta.tensors_saved_for_backwards_slice
                      ]
                  )
  
              # saved activations can have different stride to eager if
              # the compiler does layout optimization. We should restride the
              # tensor passed in for compiling the backward graph using the
              # saved tensor's stride.
              for i in range(len(placeholder_list)):
                  ph_arg = placeholder_list[i]
                  if not isinstance(ph_arg, torch.Tensor):
                      continue
  
                  if forward_saved_for_backwards_strides is None:
                      continue
  
                  real_stride = None
                  # Per all_args calling convention
                  j = i - len(symint_outs_saved_for_bw)
                  if 0 <= j < len(forward_saved_for_backwards_strides):
                      real_stride = forward_saved_for_backwards_strides[
                          j
                      ]
                  if real_stride is None:
                      continue
  
                  # Comparing ph_arg.stride() with real_stride directly may
                  # cause dynamic dimensions in ph_arg being specialized to static
                  # value. Using the hints to avoid that.
                  if _get_symint_hints(ph_arg.stride()) != real_stride:
                      # Note that here we use the stride of the real tensor to
                      # restride a FakeTensor. This does not cause trouble
                      # for dynamic shape since this code path only get
                      # executed if layout optimization is enabled. And we
                      # disable layout optimization for dynamic shape right
                      # now.
                      #
                      # A solution that decide stride order based on real
                      # tensor's stride and then apply that stride order to
                      # the FakeTensor does not work smoothly since some
                      # tensor's layout is not 'dense'. E.g. mixnet_l has a
                      # tensor with size [8, 64, 112, 112] and strides
                      # (2408448, 1, 21504, 192). The solution mentioned will
                      # decide a stride of (802816, 1, 7168, 64) for this
                      # tensor which is wrong.
                      placeholder_list[i] = ph_arg.as_strided(
                          ph_arg.size(), real_stride
                      )
  
              compiled_bw_func = None
              if len(symint_outs_saved_for_bw):
                  context = (
                      torch._C._DisableAutocast
                      if disable_amp
                      else nullcontext
                  )
                  with context():
                      try:
                          compiled_bw_func = aot_config.bw_compiler(
                              bw_module, placeholder_list
                          )
                      except Exception:
                          log.warning(
                              "failed to eagerly compile backwards for dynamic, suppressing in case backwards not needed",
                              exc_info=True,
                          )
  
      saved_context = TracingContext.try_get()
  
      class CompiledFunction(torch.autograd.Function):
          compiled_fw = compiled_fw_func
          compiled_bw = compiled_bw_func
          metadata: ViewAndMutationMeta = fw_metadata  # type: ignore[assignment]
          maybe_subclass_metadata: Optional[
              SubclassMeta
          ] = maybe_subclass_meta
          num_symints_saved_for_bw = _num_symints_saved_for_bw
          _compiled_autograd_should_lift = False
  
          @staticmethod
          def _compiled_autograd_key(ctx):
              return (ctx._autograd_function_id, *ctx.symints)
  
          @staticmethod
          def forward(ctx, *deduped_flat_tensor_args):
              args = deduped_flat_tensor_args
  
              marked_dirty_inps = []
              for (
                  i
              ) in (
                  fw_metadata.mutated_graph_handled_indices_seen_by_autograd
              ):
                  ctx.mark_dirty(deduped_flat_tensor_args[i])
                  marked_dirty_inps.append(deduped_flat_tensor_args[i])
  
              if CompiledFunction.metadata.is_rng_op_functionalized:
                  # Add the seed and offset to args
                  (
                      seed,
                      offset,
                  ) = CUDARngStateHelper.get_torch_state_as_tuple()
                  args = (*args, seed, offset)
              # There is a pretty complicated calling convention around what the compiled fw returns.
              # The full list of outputs and their relative order is:
              # (*mutated_inputs, *fw_outs, *fw_intermediate_bases, *saved_tensors, *saved_symints)
              # - Note that in the synthetic bases case, mutated_inputs will correspond to an updated version
              #   of the original view, and not the synthetic base
              fw_outs = call_func_at_runtime_with_args(
                  CompiledFunction.compiled_fw,
                  args,
                  disable_amp=disable_amp,
              )
  
              num_outputs = CompiledFunction.metadata.num_outputs
              num_outputs_aliased = (
                  CompiledFunction.metadata.num_outputs_aliased
              )
              num_intermediate_bases = (
                  CompiledFunction.metadata.num_intermediate_bases
              )
              num_symints_saved_for_bw = (
                  CompiledFunction.num_symints_saved_for_bw
              )
              num_mutated_runtime_inps = (
                  CompiledFunction.metadata.num_mutated_inp_runtime_indices
              )
              num_forward_returns = (
                  CompiledFunction.metadata.num_forward_returns
              )
              num_forward = CompiledFunction.metadata.num_forward
  
              # Partitioners must put symint arguments at the end separate from tensor arguments
              tensors_saved_for_backwards = fw_outs[
                  CompiledFunction.metadata.tensors_saved_for_backwards_slice
              ]
              assert all(
                  isinstance(x, torch.Tensor)
                  for x in tensors_saved_for_backwards
              )
              # See Note [Detaching saved tensors in AOTAutograd]
              ctx.save_for_backward(
                  *(
                      x.detach() if x._is_view() else x
                      for x in tensors_saved_for_backwards
                  )
              )
              symint_outs = fw_outs[
                  CompiledFunction.metadata.symints_saved_for_backwards_slice
              ]
              assert all(
                  isinstance(
                      x, (int, float, torch.SymInt, torch.SymFloat)
                  )
                  for x in symint_outs
              ), str([type(x) for x in symint_outs])
              ctx.symints = symint_outs
  
              raw_returns = fw_outs[0:num_forward_returns]
  
              # Wrap all autograd.Function.forward() outputs that are aliases
              # so that autograd.Function doesn't treat them as tensors
              if num_mutated_runtime_inps > 0:
                  for i, idx in enumerate(
                      CompiledFunction.metadata.mutated_inp_runtime_indices
                  ):
                      # We could make this faster by only looping over inputs with metadata-only mutations
                      # (instead of looping over inputs with either data or metadata mutations), but there shouldn't be many.
                      info = CompiledFunction.metadata.input_info[idx]
                      if (
                          info.mutates_metadata
                          and not info.mutates_data
                      ):
                          raw_returns[i] = TensorAlias(raw_returns[i])
  
                  if config.debug_assert:
                      user_mutated_inputs_raw = raw_returns[
                          0:num_mutated_runtime_inps
                      ]
                      mut_inp_infos = [
                          x
                          for x in CompiledFunction.metadata.input_info
                          if x.mutates_data or x.mutates_metadata
                      ]
                      assert len(user_mutated_inputs_raw) == len(
                          mut_inp_infos
                      )
  
              if CompiledFunction.metadata.num_unsafe_view_outputs > 0:
                  for (
                      idx
                  ) in (
                      CompiledFunction.metadata.unsafe_view_out_indices
                  ):
                      raw_return_idx = num_mutated_runtime_inps + idx
                      o = raw_returns[raw_return_idx]
                      raw_returns[
                          raw_return_idx
                      ] = torch.ops.aten._unsafe_view(o, o.shape)
  
              if num_outputs_aliased > 0:
                  for (
                      idx
                  ) in CompiledFunction.metadata.aliased_out_indices:
                      raw_return_idx = num_mutated_runtime_inps + idx
                      raw_returns[raw_return_idx] = TensorAlias(
                          raw_returns[raw_return_idx]
                      )
  
                  if config.debug_assert:
                      intermediates_raw = raw_returns[
                          num_mutated_runtime_inps + num_outputs :
                      ]
                      assert not any(
                          isinstance(x, TensorAlias)
                          for x in intermediates_raw
                      )
  
              # invariant: intermediate bases always require gradients, so we don't have to
              # consider marking them as non-differentiable.
              raw_returns_not_including_intermediate_bases = (
                  raw_returns[: num_mutated_runtime_inps + num_outputs]
              )
              raw_returns_meta = [
                  x
                  for x in CompiledFunction.metadata.input_info
                  if x.mutation_type == MutationType.MUTATED_OUT_GRAPH
              ] + CompiledFunction.metadata.output_info
  
              fw_outs_not_requiring_grad = [
                  x
                  for (i, x) in enumerate(
                      raw_returns_not_including_intermediate_bases
                  )
                  if isinstance(x, torch.Tensor)
                  and not raw_returns_meta[i].requires_grad
              ]
              ctx.mark_non_differentiable(*fw_outs_not_requiring_grad)
              ctx._materialize_non_diff_grads = False
  
              functionalized_rng_runtime_epilogue(
                  CompiledFunction.metadata,
                  fw_outs[num_forward_returns:num_forward],
                  return_new_outs=False,
              )
              return tuple(raw_returns) + tuple(marked_dirty_inps)
  
          @staticmethod
          def backward(ctx, *flat_args):
              # Calling convention: we expect a grad_out passed to the backward:
              # - for every output of the fw that does *not* alias an input or graph intermediate
              # - for every updated_input generated by the fw that does *not* alias an input (aka only data-mutations)
              # - for every graph intermediate that we need to use to generate an output later.
              # The other outputs in the autograd.Function.forward that do *not* show up in the backward include:
              # - outputs that alias inputs or graph intermediates
              # - updated inputs due to metadata-only mutations.
              # We need to return them in the forward, but ensure that they all do not get gradients in the backward,
              # and we filter them out here before passing the remaining grad_outputs into the compiled backward.
              num_intermediate_bases = (
                  CompiledFunction.metadata.num_intermediate_bases
              )
              num_graph_handled_inputs = (
                  CompiledFunction.metadata.num_mutated_graph_handled_indices_seen_by_autograd
              )
              num_mutated_runtime_inps = (
                  CompiledFunction.metadata.num_mutated_inp_runtime_indices
              )
              expected_grad_outs = (
                  CompiledFunction.metadata.num_outputs
                  + num_mutated_runtime_inps
                  + num_intermediate_bases
              )
              deterministic = CompiledFunction.metadata.deterministic
              global_deterministic = (
                  torch.are_deterministic_algorithms_enabled()
              )
              if deterministic is not None:
                  torch._check(
                      not (not deterministic and global_deterministic),
                      lambda: (
                          "This compiled backward function is being run with "
                          "torch.use_deterministic_algorithms(True), "
                          "but it was previously generated during the forward function while "
                          "torch.use_deterministic_algorithms(False) was set."
                      ),
                  )
  
              if num_graph_handled_inputs > 0:
                  flat_args = flat_args[:-num_graph_handled_inputs]
              assert len(flat_args) == expected_grad_outs
              out_info = CompiledFunction.metadata.output_info
  
              inp_tangents, out_tangents, intermediate_base_tangents = (
                  flat_args[0:num_mutated_runtime_inps],
                  flat_args[
                      num_mutated_runtime_inps : num_mutated_runtime_inps
                      + CompiledFunction.metadata.num_outputs
                  ],
                  flat_args[
                      num_mutated_runtime_inps
                      + CompiledFunction.metadata.num_outputs :
                  ],
              )
              # input_info contains info on *every* input,
              # But in the backward(), we are only given grad outputs for every mutated input
              # We then need to filter out the grad outputs that correspond to metadata-only mutations or don't require grad
              input_info = CompiledFunction.metadata.input_info
              inp_tangents_filtered = [
                  x
                  for x, info_idx in zip(
                      inp_tangents,
                      CompiledFunction.metadata.mutated_inp_runtime_indices,
                  )
                  if input_info[info_idx].mutates_data
                  and input_info[info_idx].requires_grad
              ]
              # We also need to filter out grad outputs that correspond to outputs aliasing inputs/intermediates
              out_tangents_filtered = [
                  x
                  for x, info in zip(out_tangents, out_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 always require gradients, and always participate in the backward graph.
              flat_bw_args_with_grads = [
                  *inp_tangents_filtered,
                  *out_tangents_filtered,
                  *intermediate_base_tangents,
              ]
              num_flat_bw_args_with_grads = len(flat_bw_args_with_grads)
  
              # sanity asserts
              # metadata_only_inps = [
              #     x for x, info_idx in zip(inp_tangents, mutated_inp_indices)
              #     if not input_info[info_idx].mutates_data
              # ]
              # aliased_outputs = [
              #     x for x, info in zip(out_tangents, out_info) if info.output_type != OutputType.non_alias]
              # assert all(x is None for x in metadata_only_inps)
              # assert all(x is None for x in aliased_outputs)
  
              rng_args = []
              if CompiledFunction.metadata.is_rng_op_functionalized:
                  # Add the seed and offset to args
                  rng_args = (
                      CUDARngStateHelper.get_torch_state_as_tuple()
                  )
  
              all_args = [
                  *ctx.symints,
                  *ctx.saved_tensors,
                  *flat_bw_args_with_grads,
                  *rng_args,
              ]
              del flat_bw_args_with_grads
  
              tangents_start_idx = (
                  len(all_args)
                  - num_flat_bw_args_with_grads
                  - len(rng_args)
              )
              tangents_end_idx = len(all_args) - len(rng_args)
  
              # Note: [AOTAutograd Backward Guards]
              # During AOTDispatch, we eagerly create and trace out a joint fw-bw graph.
              # Doing so requires us to "guess" about some of the metadata of our grad_outputs.
              #
              # In particular: if an output to the forward is a plain tensor or a subclass,
              # its corresponding grad_output in the backward **may or may not** be
              # a plain tensor or a subclass. The main cases are:
              # (1) If an output is a plain tensor, its grad_out will also be a plain tensor,
              #     *unless* the output is used in some subclass compute later in the forward graph,
              #     which will cause its grad_output to become a subclass
              # (2) If an output is a subclass, its grad_out will also be a subclass,
              #     *unless* the output of the forward did not actually participate in the gradient computation,
              #     in which case autograd will insert a plain tensor of zeros for the grad_output.
              #     We could avoid this case with `torch.autograd.Function.set_materialize_grads`,
              #     although this is not turned on today in AOTAutgrad and would require more work.
              #
              # Today, we make a guess on subclass-ness based on the above examples,
              # and hard-error in the backward if we guessed wrong.
              #
              # In the future, we should add backward guards that would allow us to
              # properly handle this case instead of erroring: we would need to retrace the backward graph,
              # since we might produce an entirely different trace if our grad_outputs are subclass or not.
              assert (
                  len(CompiledFunction.metadata.output_types)
                  == num_flat_bw_args_with_grads
              )
              grad_output_types = [
                  type(x)
                  for x in all_args[-num_flat_bw_args_with_grads:]
              ]
              # In general, we can add more asserts/guards here for when we partitioned
              # with incorrect assumptions about the grad_outputs.
              # Normalize FakeTensor -> torch.Tensor
              # - during tracing our types are FakeTensor
              # - at runtime in the backward our types are torch.Tensor...
              # - unless we're running compiled backward, in which case they are also FakeTensor
              grad_output_types_ = [
                  torch.Tensor if x is FakeTensor else x
                  for x in grad_output_types
              ]
              assert (
                  grad_output_types_
                  == CompiledFunction.metadata.output_types
              ), f"""\
  We incorrectly attempted to compile the backward with incorrect subclass metadata.
  If you run into this error, please file an issue.
  Expected grad_output types: {str(CompiledFunction.metadata.output_types)}
  Got grad_output types: {str(grad_output_types)}"""
  
              # TODO: figure out how to refactor the backward properly so I can use aot_dispatch_subclass_wrapper() here.
              if CompiledFunction.maybe_subclass_metadata is not None:
                  # Get the number of tangents after unwrapping
                  len_tangents = len(
                      unwrap_tensor_subclasses(
                          all_args[tangents_start_idx:tangents_end_idx],
                          is_joint_structure=False,
                      )
                  )
                  all_args = unwrap_tensor_subclasses(
                      all_args, is_joint_structure=False
                  )
                  tangents_start_idx = (
                      len(all_args) - len_tangents - len(rng_args)
                  )
                  tangents_end_idx = tangents_start_idx + len_tangents
  
              # Make the tangents contiguous. Note that we must do this after subclass desugaring
              # because inputs to inductor have to be contiguous
              all_args = [
                  t.contiguous()
                  if tangents_start_idx <= i < tangents_end_idx
                  else t
                  for i, t in enumerate(all_args)
              ]
  
              def call_compiled_backward():
                  if ctx._is_compiled_autograd_tracing():
                      # For compiled autograd, run raw FX graph so that it can be inlined into the larger graph
                      symints = ctx._get_compiled_autograd_symints()
                      assert len(symints) == len(ctx.symints)
                      all_args[: len(symints)] = symints
                      context = (
                          torch._C._DisableAutocast
                          if disable_amp
                          else nullcontext
                      )
                      with context():
                          out = normalize_as_list(bw_module(*all_args))
                      out = functionalized_rng_runtime_epilogue(
                          CompiledFunction.metadata, out
                      )
                      return tuple(out)
                  ctx.maybe_clear_saved_tensors()
                  if CompiledFunction.compiled_bw is None:
                      context = (
                          torch._C._DisableAutocast
                          if disable_amp
                          else nullcontext
                      )
                      with tracing(
                          saved_context
                      ), context(), track_graph_compiling(
                          aot_config, "backward"
                      ):
                          CompiledFunction.compiled_bw = (
                              aot_config.bw_compiler(
                                  bw_module, placeholder_list
                              )
                          )
  
                  out = call_func_at_runtime_with_args(
                      CompiledFunction.compiled_bw,
                      all_args,
                      steal_args=True,
                      disable_amp=disable_amp,
                  )
  
                  out = functionalized_rng_runtime_epilogue(
                      CompiledFunction.metadata, out
                  )
                  return tuple(out)
  
              if torch.is_grad_enabled() and any(
                  t.requires_grad
                  for t in all_args
                  if isinstance(t, torch.Tensor)
              ):
                  # Ensure that the graph is connected, and error if double backward is performed.
                  # See comment for why once_differentiable is not sufficient:
                  # https://github.com/pytorch/pytorch/pull/92348/files#r1072962107
                  class CompiledFunctionBackward(
                      torch.autograd.Function
                  ):
                      # CompiledFunctionBackward is not yet supported in dynamo skipfiles
                      _compiled_autograd_should_lift = False
  
                      @staticmethod
                      def forward(ctx, *unused_args):
                          outs = call_compiled_backward()
                          # TODO: figure out how to refactor the backward properly so I can use aot_dispatch_subclass_wrapper() here.
                          if (
                              CompiledFunction.maybe_subclass_metadata
                              is not None
                          ):
                              assert (
                                  CompiledFunction.maybe_subclass_metadata.grad_input_metas
                                  is not None
                              )
                              outs_wrapped = wrap_tensor_subclasses(
                                  outs,
                                  subclass_metas=CompiledFunction.maybe_subclass_metadata.grad_input_metas,
                              )
                              return outs_wrapped
                          return outs
  
                      @staticmethod
                      def backward(ctx, *args):
                          raise RuntimeError(
                              "torch.compile with aot_autograd does not currently support double backward"
                          )
  
                  CompiledFunctionBackward._compiled_autograd_key = (  # type: ignore[method-assign]
                      CompiledFunction._compiled_autograd_key
                  )
  
                  # Pass args even though they're unused, so that the graph is built
                  out = CompiledFunctionBackward.apply(*all_args)
              else:
                  out = call_compiled_backward()
  
              # TODO: figure out how to refactor the backward properly so I can use aot_dispatch_subclass_wrapper() here.
              if CompiledFunction.maybe_subclass_metadata is not None:
                  assert (
                      CompiledFunction.maybe_subclass_metadata.grad_input_metas
                      is not None
                  )
                  outs_wrapped = wrap_tensor_subclasses(
                      out,
                      subclass_metas=CompiledFunction.maybe_subclass_metadata.grad_input_metas,
                  )
                  return outs_wrapped
              return out
  
      compiled_function = create_runtime_wrapper(
          CompiledFunction.apply,
          runtime_metadata=fw_metadata,
          indices_of_inps_to_detach=_indices_of_inps_to_detach,
          trace_joint=True,
          keep_input_mutations=aot_config.keep_inference_input_mutations,
          disable_amp=disable_amp,
      )
  
      if not config.debug_assert:
          return compiled_function
  
      flat_requires_grad = [
          a.requires_grad if isinstance(a, Tensor) else None
          for a in flat_args
      ]
  
      @wraps(compiled_function)
      def debug_compiled_function(*args):
          # TODO: Check aliasing relationships
          # TODO: Check strides for metadata mutation
          # (NB: ideally, this logic is factored out of this function and
          # you move these debug checks there)
  
          # Check requires grad.  Bad case is when we compiled with
          # requires_grad = False, but input requires_grad = True
          # (vice versa is OK; we compute a gradient and then throw
          # it away when it hits the input.)
          for i, a in enumerate(args):
              can_require_grad = flat_requires_grad[i]
              if can_require_grad is None:
                  assert not isinstance(a, Tensor)
              elif not can_require_grad:
                  assert not a.requires_grad, format_guard_bug_msg(
                      aot_config,
                      f"{describe_input(i, aot_config)} would not require grad",
                  )
  
          return compiled_function(*args)
  
      return debug_compiled_function
  
imports: import logging
  from contextlib import nullcontext
  from functools import wraps
  from typing import Any, List, Optional
  import torch
  import torch.utils._pytree
  import torch.utils.dlpack
  from torch import Tensor
  from torch._dynamo.utils import lazy_format_graph_code
  from torch._guards import detect_fake_mode, tracing, TracingContext
  from torch._logging import getArtifactLogger
  from torch._prims_common import CUDARngStateHelper
  from torch._subclasses import FakeTensor
  from torch.fx.experimental.proxy_tensor import is_sym_node
  from torch.fx.experimental.symbolic_shapes import fx_placeholder_vals
  from ..None import config
  from .dispatch_and_compile_graph import aot_dispatch_autograd_graph, aot_dispatch_base_graph
  from .logging_utils import describe_input, format_guard_bug_msg, track_graph_compiling
  from .runtime_wrappers import aot_dispatch_subclass_wrapper, create_runtime_wrapper, functionalized_rng_runtime_epilogue
  from .schemas import AOTConfig, MutationType, OutputType, SubclassMeta, TensorAlias, ViewAndMutationMeta
  from .subclass_utils import unwrap_tensor_subclasses, wrap_tensor_subclasses
  from .utils import _get_symint_hints, call_func_at_runtime_with_args, make_boxed_func, normalize_as_list, strict_zip
label_desc: Use image node_id 2 for calling a global function with example usage: aot_dispatch_autograd(flat_fn, flat_args, aot_config) and returns: debug_compiled_function, compiled_function, compiled_function, ctx,  unknown, unknown, out, tuple, outs_wrapped, tuple, outs, outs_wrapped