code: def export(
      model: Union[
          torch.nn.Module,
          torch.jit.ScriptModule,
          torch.jit.ScriptFunction,
      ],
      args: Union[Tuple[Any, ...], torch.Tensor],
      f: Union[str, io.BytesIO],
      export_params: bool = True,
      verbose: bool = False,
      training: _C_onnx.TrainingMode = _C_onnx.TrainingMode.EVAL,
      input_names: Optional[Sequence[str]] = None,
      output_names: Optional[Sequence[str]] = None,
      operator_export_type: _C_onnx.OperatorExportTypes = _C_onnx.OperatorExportTypes.ONNX,
      opset_version: Optional[int] = None,
      do_constant_folding: bool = True,
      dynamic_axes: Optional[
          Union[
              Mapping[str, Mapping[int, str]],
              Mapping[str, Sequence[int]],
          ]
      ] = None,
      keep_initializers_as_inputs: Optional[bool] = None,
      custom_opsets: Optional[Mapping[str, int]] = None,
      export_modules_as_functions: Union[
          bool, Collection[Type[torch.nn.Module]]
      ] = False,
      autograd_inlining: Optional[bool] = True,
  ) -> None:
      r"""Exports a model into ONNX format.
  
      If ``model`` is not a :class:`torch.jit.ScriptModule` nor a
      :class:`torch.jit.ScriptFunction`, this runs
      ``model`` once in order to convert it to a TorchScript graph to be exported
      (the equivalent of :func:`torch.jit.trace`). Thus this has the same limited support
      for dynamic control flow as :func:`torch.jit.trace`.
  
      Args:
          model (:class:`torch.nn.Module`, :class:`torch.jit.ScriptModule` or :class:`torch.jit.ScriptFunction`):
              the model to be exported.
          args (tuple or torch.Tensor):
  
              args can be structured either as:
  
              1. ONLY A TUPLE OF ARGUMENTS::
  
                  args = (x, y, z)
  
              The tuple should contain model inputs such that ``model(*args)`` is a valid
              invocation of the model. Any non-Tensor arguments will be hard-coded into the
              exported model; any Tensor arguments will become inputs of the exported model,
              in the order they occur in the tuple.
  
              2. A TENSOR::
  
                  args = torch.Tensor([1])
  
              This is equivalent to a 1-ary tuple of that Tensor.
  
              3. A TUPLE OF ARGUMENTS ENDING WITH A DICTIONARY OF NAMED ARGUMENTS::
  
                  args = (
                      x,
                      {
                          "y": input_y,
                          "z": input_z
                      }
                  )
  
              All but the last element of the tuple will be passed as non-keyword arguments,
              and named arguments will be set from the last element. If a named argument is
              not present in the dictionary, it is assigned the default value, or None if a
              default value is not provided.
  
              .. note::
                  If a dictionary is the last element of the args tuple, it will be
                  interpreted as containing named arguments. In order to pass a dict as the
                  last non-keyword arg, provide an empty dict as the last element of the args
                  tuple. For example, instead of::
  
                      torch.onnx.export(
                          model,
                          (
                              x,
                              # WRONG: will be interpreted as named arguments
                              {y: z}
                          ),
                          "test.onnx.pb"
                      )
  
                  Write::
  
                      torch.onnx.export(
                          model,
                          (
                              x,
                              {y: z},
                              {}
                          ),
                          "test.onnx.pb"
                      )
  
          f: a file-like object (such that ``f.fileno()`` returns a file descriptor)
              or a string containing a file name.  A binary protocol buffer will be written
              to this file.
          export_params (bool, default True): if True, all parameters will
              be exported. Set this to False if you want to export an untrained model.
              In this case, the exported model will first take all of its parameters
              as arguments, with the ordering as specified by ``model.state_dict().values()``
          verbose (bool, default False): if True, prints a description of the
              model being exported to stdout. In addition, the final ONNX graph will include the
              field ``doc_string``` from the exported model which mentions the source code locations
              for ``model``. If True, ONNX exporter logging will be turned on.
          training (enum, default TrainingMode.EVAL):
              * ``TrainingMode.EVAL``: export the model in inference mode.
              * ``TrainingMode.PRESERVE``: export the model in inference mode if model.training is
                  False and in training mode if model.training is True.
              * ``TrainingMode.TRAINING``: export the model in training mode. Disables optimizations
                  which might interfere with training.
          input_names (list of str, default empty list): names to assign to the
              input nodes of the graph, in order.
          output_names (list of str, default empty list): names to assign to the
              output nodes of the graph, in order.
          operator_export_type (enum, default OperatorExportTypes.ONNX):
  
              * ``OperatorExportTypes.ONNX``: Export all ops as regular ONNX ops
                  (in the default opset domain).
              * ``OperatorExportTypes.ONNX_FALLTHROUGH``: Try to convert all ops
                  to standard ONNX ops in the default opset domain. If unable to do so
                  (e.g. because support has not been added to convert a particular torch op to ONNX),
                  fall back to exporting the op into a custom opset domain without conversion. Applies
                  to `custom ops <https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html>`_
                  as well as ATen ops. For the exported model to be usable, the runtime must support
                  these non-standard ops.
              * ``OperatorExportTypes.ONNX_ATEN``: All ATen ops (in the TorchScript namespace "aten")
                  are exported as ATen ops (in opset domain "org.pytorch.aten").
                  `ATen <https://pytorch.org/cppdocs/#aten>`_ is PyTorch's built-in tensor library, so
                  this instructs the runtime to use PyTorch's implementation of these ops.
  
                  .. warning::
  
                      Models exported this way are probably runnable only by Caffe2.
  
                      This may be useful if the numeric differences in implementations of operators are
                      causing large differences in behavior between PyTorch and Caffe2 (which is more
                      common on untrained models).
  
              * ``OperatorExportTypes.ONNX_ATEN_FALLBACK``: Try to export each ATen op
                  (in the TorchScript namespace "aten") as a regular ONNX op. If we are unable to do so
                  (e.g. because support has not been added to convert a particular torch op to ONNX),
                  fall back to exporting an ATen op. See documentation on OperatorExportTypes.ONNX_ATEN for
                  context.
                  For example::
  
                      graph(%0 : Float):
                      %3 : int = prim::Constant[value=0]()
                      # conversion unsupported
                      %4 : Float = aten::triu(%0, %3)
                      # conversion supported
                      %5 : Float = aten::mul(%4, %0)
                      return (%5)
  
                  Assuming ``aten::triu`` is not supported in ONNX, this will be exported as::
  
                      graph(%0 : Float):
                      %1 : Long() = onnx::Constant[value={0}]()
                      # not converted
                      %2 : Float = aten::ATen[operator="triu"](%0, %1)
                      # converted
                      %3 : Float = onnx::Mul(%2, %0)
                      return (%3)
  
                  If PyTorch was built with Caffe2 (i.e. with ``BUILD_CAFFE2=1``), then
                  Caffe2-specific behavior will be enabled, including special support
                  for ops are produced by the modules described in
                  `Quantization <https://pytorch.org/docs/stable/quantization.html>`_.
  
                  .. warning::
  
                      Models exported this way are probably runnable only by Caffe2.
  
          opset_version (int, default 17): The version of the
              `default (ai.onnx) opset <https://github.com/onnx/onnx/blob/master/docs/Operators.md>`_
              to target. Must be >= 7 and <= 17.
          do_constant_folding (bool, default True): Apply the constant-folding optimization.
              Constant-folding will replace some of the ops that have all constant inputs
              with pre-computed constant nodes.
          dynamic_axes (dict[string, dict[int, string]] or dict[string, list(int)], default empty dict):
  
              By default the exported model will have the shapes of all input and output tensors
              set to exactly match those given in ``args``. To specify axes of tensors as
              dynamic (i.e. known only at run-time), set ``dynamic_axes`` to a dict with schema:
  
              * KEY (str): an input or output name. Each name must also be provided in ``input_names`` or
                  ``output_names``.
              * VALUE (dict or list): If a dict, keys are axis indices and values are axis names. If a
                  list, each element is an axis index.
  
              For example::
  
                  class SumModule(torch.nn.Module):
                      def forward(self, x):
                          return torch.sum(x, dim=1)
  
                  torch.onnx.export(
                      SumModule(),
                      (torch.ones(2, 2),),
                      "onnx.pb",
                      input_names=["x"],
                      output_names=["sum"]
                  )
  
              Produces::
  
                  input {
                    name: "x"
                    ...
                        shape {
                          dim {
                            dim_value: 2  # axis 0
                          }
                          dim {
                            dim_value: 2  # axis 1
                  ...
                  output {
                    name: "sum"
                    ...
                        shape {
                          dim {
                            dim_value: 2  # axis 0
                  ...
  
              While::
  
                  torch.onnx.export(
                      SumModule(),
                      (torch.ones(2, 2),),
                      "onnx.pb",
                      input_names=["x"],
                      output_names=["sum"],
                      dynamic_axes={
                          # dict value: manually named axes
                          "x": {0: "my_custom_axis_name"},
                          # list value: automatic names
                          "sum": [0],
                      }
                  )
  
              Produces::
  
                  input {
                    name: "x"
                    ...
                        shape {
                          dim {
                            dim_param: "my_custom_axis_name"  # axis 0
                          }
                          dim {
                            dim_value: 2  # axis 1
                  ...
                  output {
                    name: "sum"
                    ...
                        shape {
                          dim {
                            dim_param: "sum_dynamic_axes_1"  # axis 0
                  ...
  
          keep_initializers_as_inputs (bool, default None): If True, all the
              initializers (typically corresponding to parameters) in the
              exported graph will also be added as inputs to the graph. If False,
              then initializers are not added as inputs to the graph, and only
              the non-parameter inputs are added as inputs.
              This may allow for better optimizations (e.g. constant folding) by
              backends/runtimes.
  
              If True, `deduplicate_initializers` pass will not be executed. This means
              initializers with duplicated values will not be deduplicated and
              will be treated as distinct inputs to the graph. This allows different
              input initializers to be supplied at the runtime following export.
  
              If ``opset_version < 9``, initializers MUST be part of graph
              inputs and this argument will be ignored and the behavior will be
              equivalent to setting this argument to True.
  
              If None, then the behavior is chosen automatically as follows:
  
              * If ``operator_export_type=OperatorExportTypes.ONNX``, the behavior is equivalent
                  to setting this argument to False.
              * Else, the behavior is equivalent to setting this argument to True.
  
          custom_opsets (dict[str, int], default empty dict): A dict with schema:
  
              * KEY (str): opset domain name
              * VALUE (int): opset version
  
              If a custom opset is referenced by ``model`` but not mentioned in this dictionary,
              the opset version is set to 1. Only custom opset domain name and version should be
              indicated through this argument.
  
          export_modules_as_functions (bool or set of type of nn.Module, default False): Flag to enable
              exporting all ``nn.Module`` forward calls as local functions in ONNX. Or a set to indicate the
              particular types of modules to export as local functions in ONNX.
              This feature requires ``opset_version`` >= 15, otherwise the export will fail. This is because
              ``opset_version`` < 15 implies IR version < 8, which means no local function support.
              Module variables will be exported as function attributes. There are two categories of function
              attributes.
  
              1. Annotated attributes: class variables that have type annotations via
              `PEP 526-style <https://www.python.org/dev/peps/pep-0526/#class-and-instance-variable-annotations>`_
              will be exported as attributes.
              Annotated attributes are not used inside the subgraph of ONNX local function because
              they are not created by PyTorch JIT tracing, but they may be used by consumers
              to determine whether or not to replace the function with a particular fused kernel.
  
              2. Inferred attributes: variables that are used by operators inside the module. Attribute names
              will have prefix "inferred::". This is to differentiate from predefined attributes retrieved from
              python module annotations. Inferred attributes are used inside the subgraph of ONNX local function.
  
              * ``False`` (default): export ``nn.Module`` forward calls as fine grained nodes.
              * ``True``: export all ``nn.Module`` forward calls as local function nodes.
              * Set of type of nn.Module: export ``nn.Module`` forward calls as local function nodes,
                  only if the type of the ``nn.Module`` is found in the set.
  
          autograd_inlining (bool, default True): Flag used to control whether to inline autograd functions.
              Refer to https://github.com/pytorch/pytorch/pull/74765 for more details.
  
      Raises:
          :class:`torch.onnx.errors.CheckerError`: If the ONNX checker detects an invalid ONNX graph.
          :class:`torch.onnx.errors.UnsupportedOperatorError`: If the ONNX graph cannot be exported because it
              uses an operator that is not supported by the exporter.
          :class:`torch.onnx.errors.OnnxExporterError`: Other errors that can occur during export.
              All errors are subclasses of :class:`errors.OnnxExporterError`.
      """
  
      _export(
          model,
          args,
          f,
          export_params,
          verbose,
          training,
          input_names,
          output_names,
          operator_export_type=operator_export_type,
          opset_version=opset_version,
          do_constant_folding=do_constant_folding,
          dynamic_axes=dynamic_axes,
          keep_initializers_as_inputs=keep_initializers_as_inputs,
          custom_opsets=custom_opsets,
          export_modules_as_functions=export_modules_as_functions,
          autograd_inlining=autograd_inlining,
      )
  
imports: from __future__ import annotations
  import contextlib
  import copy
  import inspect
  import io
  import re
  import textwrap
  import typing
  import warnings
  from typing import Any, Callable, cast, Collection, Dict, List, Mapping, Optional, Sequence, Set, Tuple, Type, Union
  import torch
  import torch._C._onnx
  import torch.jit._trace
  import torch.serialization
  from torch import _C
  from torch.onnx import _constants, _exporter_states, errors, symbolic_caffe2, symbolic_helper
  from torch.onnx._globals import GLOBALS
  from torch.onnx._internal import _beartype, diagnostics, jit_utils, onnx_proto_utils, registration
label_desc: Use image node_id 6 for calling a global function with example usage: export(model, args, f, export_params, verbose, training, input_names, output_names, operator_export_type, opset_version, do_constant_folding, dynamic_axes, keep_initializers_as_inputs, custom_opsets, export_modules_as_functions, autograd_inlining) without return types