code: def aot_compile(
      f: Callable,
      args: Tuple[Any],
      kwargs: Optional[Dict[str, Any]] = None,
      *,
      constraints: Optional[List[Constraint]] = None,
      dynamic_shapes: Optional[Dict[str, Any]] = None,
      options: Optional[Dict[str, Any]] = None,
      remove_runtime_assertions: bool = False,
      disable_constraint_solver: bool = False,
  ) -> str:
      """
      Note: this function is not stable yet
  
      Traces either an nn.Module's forward function or just a callable with PyTorch
      operations inside, generates executable cpp code from the program, and returns
      the path to the generated shared library
  
      Args:
          f: the `nn.Module` or callable to trace.
  
          args: example positional inputs.
  
          kwargs: optional example keyword inputs.
  
          constraints: A optional list of constraints on the dynamic arguments specifying
              their possible range of their shapes
  
          dynamic_shapes: An experimental new feature designed to subsume ``constraints``.
              A dict mapping argument names of ``f`` to their dynamic shape
              specifications, as follows. Dynamic shape specifications can be a
              dict from dynamic dimensions to ``Dim`` types, or a tuple/list of
              ``Optional[Dim]`` corresponding to each input dimension.
  
          options: A dictionary of options to control inductor
  
          disable_constraint_solver: Whether the dim constraint solver must be disabled.
  
      Returns:
          Path to the generated shared library
      """
      if constraints is not None:
          warnings.warn(
              "The constraints field is deprecated. "
              "Please use dynamic_shapes instead."
          )
  
      from torch.export._trace import _export_to_torch_ir
      from torch._inductor.decomposition import select_decomp_table
  
      if constraints is None:
          constraints = _process_dynamic_shapes(
              f, args, kwargs, dynamic_shapes
          )
  
      if config.is_predispatch:
          gm = torch.export._trace._export(
              f, args, kwargs, constraints, pre_dispatch=True
          ).module()
      else:
          # We want to export to Torch IR here to utilize the pre_grad passes in
          # inductor, which run on Torch IR.
          gm = _export_to_torch_ir(
              f,
              args,
              kwargs,
              constraints,
              disable_constraint_solver=disable_constraint_solver,
              # Disabling this flag, because instead we can rely on the mapping
              # dynamo_flat_name_to_original_fqn which is coming from Dynamo.
              restore_fqn=False,
          )
      flat_example_inputs = pytree.arg_tree_leaves(
          *args, **(kwargs or {})
      )
  
      with torch.no_grad():
          so_path = torch._inductor.aot_compile(gm, flat_example_inputs, options)  # type: ignore[arg-type]
  
      return so_path
  
imports: import copy
  import dataclasses
  import functools
  import io
  import json
  import os
  import re
  import sys
  import types
  import warnings
  import weakref
  import zipfile
  from collections import OrderedDict
  from contextlib import contextmanager
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  from unittest.mock import patch
  import sympy
  import torch
  import torch._dynamo
  import torch.fx
  import torch.fx._pytree
  import torch.utils._pytree
  from torch._decomp import core_aten_decompositions, get_decompositions
  from torch._dispatch.python import enable_python_dispatcher
  from torch._dynamo.exc import UserError, UserErrorType
  from torch._dynamo.source import ConstantSource
  from torch._export.passes.collect_tracepoints_pass import CollectTracepointsPass
  from torch._functorch.aot_autograd import aot_export_module, GraphSignature
  from torch._functorch.eager_transforms import functionalize
  from torch._guards import detect_fake_mode
  from torch._ops import OpOverload
  from torch._subclasses.fake_tensor import FakeTensor, FakeTensorMode
  from torch._subclasses.functional_tensor import FunctionalTensor
  from torch.export.exported_program import ExportedProgram, ModuleCallEntry, ModuleCallSignature, _disable_prexisiting_fake_mode
  from torch.export.graph_signature import _sig_to_specs, ArgumentSpec, ConstantArgument, ExportGraphSignature, InputKind, InputSpec, OutputKind, OutputSpec, SymIntArgument, TensorArgument
  from torch.export.dynamic_shapes import Constraint, dynamic_dim, _process_constraints, _process_dynamic_shapes
  from torch.export._unlift import _create_stateful_graph_module
  from torch.fx import traceback
  from torch.fx._compatibility import compatibility
  from torch.fx.experimental.proxy_tensor import make_fx, maybe_disable_fake_tensor_mode
  from torch.fx.experimental.symbolic_shapes import ConstraintViolationError, GuardOnDataDependentSymNode, ShapeEnv, StrictMinMaxConstraint
  from torch.fx.graph import _PyTreeCodeGen, _PyTreeInfo
  from torch.utils._sympy.value_ranges import ValueRangeError, ValueRanges
  from .exported_program import CallSpec
  from .passes.add_runtime_assertions_for_constraints_pass import _AddRuntimeAssertionsForInlineConstraintsPass
  from .passes.lift_constant_tensor_pass import lift_constant_tensor_pass
  from .passes.remove_runtime_assertions import _RemoveRuntimeAssertionsPass
  from .passes.replace_sym_size_ops_pass import _replace_sym_size_ops_pass
  from .passes.replace_view_ops_with_view_copy_ops_pass import ReplaceViewOpsWithViewCopyOpsPass
  from .wrappers import _wrap_submodules
  from torch._inductor import config
label_desc: Use image node_id 5 for calling a global function with example usage: aot_compile(f, args, kwargs) and returns: so_path