code: def aot_dispatch_base(
      flat_fn,
      flat_args: List[Tensor],
      aot_config: AOTConfig,
      *,
      fw_metadata: ViewAndMutationMeta,
  ):
      fw_module, updated_flat_args, maybe_subclass_meta = aot_dispatch_base_graph(  # type: ignore[misc]
          flat_fn, flat_args, aot_config, fw_metadata=fw_metadata
      )
  
      disable_amp = torch._C._is_any_autocast_enabled()
      context = (
          torch._C._DisableAutocast if disable_amp else nullcontext
      )
  
      with context(), track_graph_compiling(aot_config, "inference"):
          compiler = (
              aot_config.inference_compiler
              if aot_config.inference_compiler is not None
              else aot_config.fw_compiler
          )
          if config.functionalize_rng_ops:
              # Add the seed and offset as example inputs to pass to the compiler
              fake_mode = detect_fake_mode()
              (
                  seed,
                  offset,
              ) = CUDARngStateHelper.get_torch_state_as_tuple(fake_mode)
              updated_flat_args.extend([seed, offset])
  
          if tracing_context := torch._guards.TracingContext.try_get():
              tracing_context.fw_metadata = (
                  fw_metadata
                  if maybe_subclass_meta is None
                  else maybe_subclass_meta.fw_metadata
              )
          compiled_fw = compiler(fw_module, updated_flat_args)
  
      # This boxed_call handling happens inside create_runtime_wrapper as well.
      # However, create_runtime_wrapper does not expect the rng offsets in the
      # output. So, we have to create another wrapper and take out the offset. As
      # a result, we have to account for not boxed_call compilers as well.
      if not hasattr(compiled_fw, "_boxed_call"):
          compiled_fw = make_boxed_func(compiled_fw)
  
      # Create a wrapper to set up the rng functionalize bits
      @wraps(compiled_fw)
      def rng_functionalization_wrapper(args):
          # args is a list because compiled_fw is boxed_call
          if fw_metadata.is_rng_op_functionalized:
              # Add the seed and offset to args
              (
                  seed,
                  offset,
              ) = CUDARngStateHelper.get_torch_state_as_tuple()
              args.extend([seed, offset])
              out = compiled_fw(args)
              out = functionalized_rng_runtime_epilogue(
                  fw_metadata, out
              )
              return out
          else:
              return compiled_fw(args)
  
      if maybe_subclass_meta is not None:
          compiled_fw_func = aot_dispatch_subclass_wrapper(
              rng_functionalization_wrapper,
              subclass_metas=fw_metadata.subclass_fw_graph_out_meta,
              num_fw_outs_saved_for_bw=None,
          )
      else:
          compiled_fw_func = rng_functionalization_wrapper
  
      if not hasattr(compiled_fw_func, "_boxed_call"):
          compiled_fw_func = make_boxed_func(compiled_fw_func)
  
      compiled_fn = create_runtime_wrapper(
          compiled_fw_func,
          runtime_metadata=fw_metadata,
          indices_of_inps_to_detach=[],
          trace_joint=False,
          keep_input_mutations=aot_config.keep_inference_input_mutations,
          disable_amp=disable_amp,
      )
  
      return compiled_fn
  
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 1 for calling a global function with example usage: aot_dispatch_base(flat_fn, flat_args, aot_config) and returns: compiled_fn, out, compiled_fw