code: def aot_dispatch_base_graph(
      flat_fn,
      flat_args: List[Tensor],
      aot_config: AOTConfig,
      *,
      fw_metadata: ViewAndMutationMeta,
  ) -> Union[
      Callable, Tuple[Callable, List[Any], Optional[SubclassMeta]]
  ]:
      # aot_dispatch_base requires functionalization, but doesn't need to handle as many cases as the autograd case.
      # The cases that aot_dispatch_base doesn't need to handle include:
      # - outputs that are aliases of graph intermediates
      # - outputs that are aliases of graph inputs
      # While cases that it does need to handle include:
      # - input mutations (including when inputs are aliases of each other)
      # - input metadata mutations
      fn_to_trace = fn_input_mutations_to_outputs(
          flat_fn,
          fw_metadata,
          keep_data_input_mutations=aot_config.keep_inference_input_mutations,
      )
  
      fn_to_trace, updated_flat_args = create_functionalized_fn(
          fn_to_trace,
          flat_args,
          meta=fw_metadata,
          aot_config=aot_config,
          trace_joint=False,
      )
  
      (
          fn_to_trace,
          updated_flat_args_subclasses_desugared,
          maybe_subclass_meta,
      ) = aot_dispatch_subclass(
          fn_to_trace,
          updated_flat_args,
          is_joint_structure=False,
          meta=fw_metadata,
          fw_only=flat_fn,
      )
  
      fw_module = _create_graph(
          fn_to_trace,
          updated_flat_args_subclasses_desugared,
          aot_config=aot_config,
      )
  
      # As long as we opted to remove input mutations, then
      # there should be *NO* mutating ops in the graph at this point.
      copy_count = assert_functional_graph(fw_module.graph)
  
      fw_module.graph.eliminate_dead_code()
      fw_module.recompile()
  
      copy_count2 = assert_functional_graph(fw_module.graph)
  
      assert copy_count == copy_count2
  
      if aot_config.enable_log:
          aot_graphs_log.info(
              "%s",
              lazy_format_graph_code(
                  "Forward graph", fw_module, aot_config.aot_id
              ),
          )
  
      # TODO: should factor this into a separate function for export that always only returns just the graph.
      if aot_config.is_export:
          assert (
              maybe_subclass_meta is None
          ), "aot_export_module does not support tensor subclass inputs for now."
          return fw_module
      return (
          fw_module,
          list(updated_flat_args_subclasses_desugared),
          maybe_subclass_meta,
      )
  
file_path: pytorch/torch/_functorch/_aot_autograd/dispatch_and_compile_graph.py
imports: from typing import Any, Callable, List, Optional, Tuple, Union
  import torch
  import torch.utils._pytree
  import torch.utils.dlpack
  from torch import Tensor
  from torch._dispatch.python import enable_python_dispatcher
  from torch._dynamo.utils import lazy_format_graph_code
  from torch._logging import getArtifactLogger
  from torch._subclasses.functional_tensor import FunctionalTensorMode
  from torch.fx.experimental.proxy_tensor import make_fx
  from .functional_utils import assert_functional_graph
  from .schemas import AOTConfig, SubclassMeta, ViewAndMutationMeta
  from .traced_function_transforms import aot_dispatch_subclass, create_functionalized_fn, create_joint, fn_input_mutations_to_outputs, fn_prepped_for_autograd
label_desc: Use image node_id 2 for calling a global function with example usage: aot_dispatch_base_graph(flat_fn, flat_args, aot_config) and returns: fw_module,  list,  maybe_subclass_meta, fw_module