torch/onnx/__init__.py - platform/external/pytorch - Git at Google

 import torch._C as _C

 TensorProtoDataType = _C._onnx.TensorProtoDataType
 OperatorExportTypes = _C._onnx.OperatorExportTypes
 TrainingMode = _C._onnx.TrainingMode
 PYTORCH_ONNX_CAFFE2_BUNDLE = _C._onnx.PYTORCH_ONNX_CAFFE2_BUNDLE

 ONNX_ARCHIVE_MODEL_PROTO_NAME = "__MODEL_PROTO"

 # TODO: Update these variables when there
 # is a new ir_version and producer_version
 # and use these values in the exporter
 ir_version = _C._onnx.IR_VERSION
 producer_name = "pytorch"
 producer_version = _C._onnx.PRODUCER_VERSION
 constant_folding_opset_versions = [9, 10, 11, 12, 13, 14]


 class ExportTypes:
     PROTOBUF_FILE = 1
     ZIP_ARCHIVE = 2
     COMPRESSED_ZIP_ARCHIVE = 3
     DIRECTORY = 4


 def _export(*args, **kwargs):
     from torch.onnx import utils
     result = utils._export(*args, **kwargs)
     return result


 def export(model, args, f, export_params=True, verbose=False, training=TrainingMode.EVAL,
            input_names=None, output_names=None, operator_export_type=None,
            opset_version=None, _retain_param_name=True, do_constant_folding=True,
            example_outputs=None, strip_doc_string=True, dynamic_axes=None,
            keep_initializers_as_inputs=None, custom_opsets=None, enable_onnx_checker=True,
            use_external_data_format=False):
     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 (torch.nn.Module, torch.jit.ScriptModule or 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.
         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 None):

             None usually means ``OperatorExportTypes.ONNX``.
             However if PyTorch was built with ``-DPYTORCH_ONNX_CAFFE2_BUNDLE``, None means
             ``OperatorExportTypes.ONNX_ATEN_FALLBACK``.

             * ``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 an op is in the TorchScript namespace "quantized", it will be exported
               in the ONNX opset domain "caffe2". These 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 9):
             Must be ``== _onnx_main_opset or in _onnx_stable_opsets``,
             defined in torch/onnx/symbolic_helper.py.
         do_constant_folding (bool, default False): Apply the constant-folding optimization.
             Constant-folding will replace some of the ops that have all constant inputs
             with pre-computed constant nodes.
         example_outputs (T or a tuple of T, where T is Tensor or convertible to Tensor, default None):
             Must be provided when exporting a ScriptModule or ScriptFunction, ignored otherwise.
             Used to determine the type and shape of the outputs without tracing the execution of
             the model. A single object is treated as equivalent to a tuple of one element.
         strip_doc_string (bool, default True): do not include the field
             ``doc_string``` from the exported model. Otherwise the field will mention the source
             code locations for ``model``.
         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`` (and ``example_outputs`` when that arg is
             required). 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 ``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 dictionary to indicate

             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.

         enable_onnx_checker (bool, default True): If True the onnx model checker will be run
             to ensure the exported model is a valid ONNX model.
         use_external_data_format (bool, default False): If True, then some of the model
             parameters are stored in external data files and not in the ONNX model file itself.
             Models larger than 2GB cannot be exported in one file because of size limits imposed
             by Protocol Buffers.
             For details see
             `onnx.proto <https://github.com/onnx/onnx/blob/32c7cb66/onnx/onnx.proto#L562>`_.
             If True,  argument ``f`` must be a string specifying the location of the model.
             The external data files will be stored in the same directory as ``f``.
             This argument is ignored unless ``operator_export_type=OperatorExportTypes.ONNX``.
     """

     from torch.onnx import utils
     return utils.export(model, args, f, export_params, verbose, training,
                         input_names, output_names, operator_export_type, opset_version,
                         _retain_param_name, do_constant_folding, example_outputs,
                         strip_doc_string, dynamic_axes, keep_initializers_as_inputs,
                         custom_opsets, enable_onnx_checker, use_external_data_format)


 def export_to_pretty_string(*args, **kwargs):
     r"""
     Same as :func:`export`, but returns a text representation of the exported model.
     """
     from torch.onnx import utils
     return utils.export_to_pretty_string(*args, **kwargs)


 def _export_to_pretty_string(*args, **kwargs):
     from torch.onnx import utils
     return utils._export_to_pretty_string(*args, **kwargs)


 def _optimize_trace(graph, operator_export_type):
     from torch.onnx import utils
     return utils._optimize_graph(graph, operator_export_type)


 def select_model_mode_for_export(model, mode):
     r"""
     A context manager to temporarily set the training mode of ``model``
     to ``mode``, resetting it when we exit the with-block.  A no-op if
     mode is None.

     Args:
       model: Same type and meaning as ``model`` arg to :func:`export`.
       mode: Same type and meaning as ``training`` arg to :func:`export`.
     """

     from torch.onnx import utils
     return utils.select_model_mode_for_export(model, mode)


 def _run_symbolic_function(*args, **kwargs):
     from torch.onnx import utils
     return utils._run_symbolic_function(*args, **kwargs)


 def _run_symbolic_method(*args, **kwargs):
     from torch.onnx import utils
     return utils._run_symbolic_method(*args, **kwargs)


 def is_in_onnx_export():
     r"""
     Returns True iff :func:`export` is running in the current thread
     """

     from torch.onnx import utils
     return utils.is_in_onnx_export()


 def register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version):
     r"""
     Registers ``symbolic_fn`` to handle ``symbolic_name``. See
     "Custom Operators" in the module documentation for an example usage.

     Args:
       symbolic_name (str): The name of the custom operator in "<domain>::<op>"
         format.
       symbolic_fn (Callable): A function that takes in the ONNX graph and
         the input arguments to the current operator, and returns new
         operator nodes to add to the graph.
       opset_version (int): The ONNX opset version in which to register.
     """
     from torch.onnx import utils
     utils.register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version)
	import torch._C as _C

	TensorProtoDataType = _C._onnx.TensorProtoDataType
	OperatorExportTypes = _C._onnx.OperatorExportTypes
	TrainingMode = _C._onnx.TrainingMode
	PYTORCH_ONNX_CAFFE2_BUNDLE = _C._onnx.PYTORCH_ONNX_CAFFE2_BUNDLE

	ONNX_ARCHIVE_MODEL_PROTO_NAME = "__MODEL_PROTO"

	# TODO: Update these variables when there
	# is a new ir_version and producer_version
	# and use these values in the exporter
	ir_version = _C._onnx.IR_VERSION
	producer_name = "pytorch"
	producer_version = _C._onnx.PRODUCER_VERSION
	constant_folding_opset_versions = [9, 10, 11, 12, 13, 14]


	class ExportTypes:
	PROTOBUF_FILE = 1
	ZIP_ARCHIVE = 2
	COMPRESSED_ZIP_ARCHIVE = 3
	DIRECTORY = 4


	def _export(args, *kwargs):
	from torch.onnx import utils
	result = utils._export(args, *kwargs)
	return result


	def export(model, args, f, export_params=True, verbose=False, training=TrainingMode.EVAL,
	input_names=None, output_names=None, operator_export_type=None,
	opset_version=None, _retain_param_name=True, do_constant_folding=True,
	example_outputs=None, strip_doc_string=True, dynamic_axes=None,
	keep_initializers_as_inputs=None, custom_opsets=None, enable_onnx_checker=True,
	use_external_data_format=False):
	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 (torch.nn.Module, torch.jit.ScriptModule or 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.
	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 None):

	None usually means ``OperatorExportTypes.ONNX``.
	However if PyTorch was built with ``-DPYTORCH_ONNX_CAFFE2_BUNDLE``, None means
	``OperatorExportTypes.ONNX_ATEN_FALLBACK``.

	* ``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 an op is in the TorchScript namespace "quantized", it will be exported
	in the ONNX opset domain "caffe2". These 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 9):
	Must be ``== _onnx_main_opset or in _onnx_stable_opsets``,
	defined in torch/onnx/symbolic_helper.py.
	do_constant_folding (bool, default False): Apply the constant-folding optimization.
	Constant-folding will replace some of the ops that have all constant inputs
	with pre-computed constant nodes.
	example_outputs (T or a tuple of T, where T is Tensor or convertible to Tensor, default None):
	Must be provided when exporting a ScriptModule or ScriptFunction, ignored otherwise.
	Used to determine the type and shape of the outputs without tracing the execution of
	the model. A single object is treated as equivalent to a tuple of one element.
	strip_doc_string (bool, default True): do not include the field
	``doc_string``` from the exported model. Otherwise the field will mention the source
	code locations for ``model``.
	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`` (and ``example_outputs`` when that arg is
	required). 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 ``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 dictionary to indicate

	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.

	enable_onnx_checker (bool, default True): If True the onnx model checker will be run
	to ensure the exported model is a valid ONNX model.
	use_external_data_format (bool, default False): If True, then some of the model
	parameters are stored in external data files and not in the ONNX model file itself.
	Models larger than 2GB cannot be exported in one file because of size limits imposed
	by Protocol Buffers.
	For details see
	`onnx.proto <https://github.com/onnx/onnx/blob/32c7cb66/onnx/onnx.proto#L562>`_.
	If True, argument ``f`` must be a string specifying the location of the model.
	The external data files will be stored in the same directory as ``f``.
	This argument is ignored unless ``operator_export_type=OperatorExportTypes.ONNX``.
	"""

	from torch.onnx import utils
	return utils.export(model, args, f, export_params, verbose, training,
	input_names, output_names, operator_export_type, opset_version,
	_retain_param_name, do_constant_folding, example_outputs,
	strip_doc_string, dynamic_axes, keep_initializers_as_inputs,
	custom_opsets, enable_onnx_checker, use_external_data_format)


	def export_to_pretty_string(args, *kwargs):
	r"""
	Same as :func:`export`, but returns a text representation of the exported model.
	"""
	from torch.onnx import utils
	return utils.export_to_pretty_string(args, *kwargs)


	def _export_to_pretty_string(args, *kwargs):
	from torch.onnx import utils
	return utils._export_to_pretty_string(args, *kwargs)


	def _optimize_trace(graph, operator_export_type):
	from torch.onnx import utils
	return utils._optimize_graph(graph, operator_export_type)


	def select_model_mode_for_export(model, mode):
	r"""
	A context manager to temporarily set the training mode of ``model``
	to ``mode``, resetting it when we exit the with-block. A no-op if
	mode is None.

	Args:
	model: Same type and meaning as ``model`` arg to :func:`export`.
	mode: Same type and meaning as ``training`` arg to :func:`export`.
	"""

	from torch.onnx import utils
	return utils.select_model_mode_for_export(model, mode)


	def _run_symbolic_function(args, *kwargs):
	from torch.onnx import utils
	return utils._run_symbolic_function(args, *kwargs)


	def _run_symbolic_method(args, *kwargs):
	from torch.onnx import utils
	return utils._run_symbolic_method(args, *kwargs)


	def is_in_onnx_export():
	r"""
	Returns True iff :func:`export` is running in the current thread
	"""

	from torch.onnx import utils
	return utils.is_in_onnx_export()


	def register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version):
	r"""
	Registers ``symbolic_fn`` to handle ``symbolic_name``. See
	"Custom Operators" in the module documentation for an example usage.

	Args:
	symbolic_name (str): The name of the custom operator in "<domain>::<op>"
	format.
	symbolic_fn (Callable): A function that takes in the ONNX graph and
	the input arguments to the current operator, and returns new
	operator nodes to add to the graph.
	opset_version (int): The ONNX opset version in which to register.
	"""
	from torch.onnx import utils
	utils.register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version)