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]


 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, aten=False, export_raw_ir=False,
            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"""
     Export a model into ONNX format.  This exporter runs your model
     once in order to get a trace of its execution to be exported;
     at the moment, it supports a limited set of dynamic models (e.g., RNNs.)

     Args:
         model (torch.nn.Module): the model to be exported.
         args (tuple of arguments or torch.Tensor, a dictionary consisting of named arguments (optional)):
             a dictionary to specify the input to the corresponding named parameter:
             - KEY: str, named parameter
             - VALUE: corresponding input
             args can be structured either as:

             1. ONLY A TUPLE OF ARGUMENTS or torch.Tensor::

                 "args = (x, y, z)"

             The inputs to the model, e.g., 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 args. If args is a Tensor, this is equivalent to having
             called it with a 1-ary tuple of that Tensor.

             2. A TUPLE OF ARGUEMENTS WITH A DICTIONARY OF NAMED PARAMETERS::

                 "args = (x,
                         {
                         'y': input_y,
                         'z': input_z
                         })"

             The inputs to the model are structured as a tuple consisting of
             non-keyword arguments and the last value of this tuple being a dictionary
             consisting of named parameters and the corresponding inputs as key-value pairs.
             If certain named argument is not present in the dictionary, it is assigned
             the default value, or None if default value is not provided.

             Cases in which an dictionary input is the last input of the args tuple
             would cause a conflict when a dictionary of named parameters is used.
             The model below provides such an example.

                 class Model(torch.nn.Module):
                     def forward(self, k, x):
                         ...
                         return x

                 m = Model()
                 k = torch.randn(2, 3)
                 x = {torch.tensor(1.): torch.randn(2, 3)}

                 In the previous iteration, the call to export API would look like

                     torch.onnx.export(model, (k, x), 'test.onnx')

                 This would work as intended. However, the export function
                 would now assume that the `x` input is intended to represent the optional
                 dictionary consisting of named arguments. In order to prevent this from being
                 an issue a constraint is placed to provide an empty dictionary as the last
                 input in the tuple args in such cases. The new call would look like this.

                     torch.onnx.export(model, (k, x, {}), 'test.onnx')

         f: a file-like object (has to implement fileno that returns a file descriptor)
             or a string containing a file name.  A binary Protobuf will be written
             to this file.
         export_params (bool, default True): if specified, 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, the ordering as specified by ``model.state_dict().values()``
         verbose (bool, default False): if specified, we will print out a debug
             description of the trace being exported.
         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 to a training friendly mode if model.training is True.
             TrainingMode.TRAINING: export the model in a training friendly mode.
         input_names(list of strings, default empty list): names to assign to the
             input nodes of the graph, in order
         output_names(list of strings, default empty list): names to assign to the
             output nodes of the graph, in order
         aten (bool, default False): [DEPRECATED. use operator_export_type] export the
             model in aten mode. If using aten mode, all the ops original exported
             by the functions in symbolic_opset<version>.py are exported as ATen ops.
         export_raw_ir (bool, default False): [DEPRECATED. use operator_export_type]
             export the internal IR directly instead of converting it to ONNX ops.
         operator_export_type (enum, default OperatorExportTypes.ONNX):
             OperatorExportTypes.ONNX: All ops are exported as regular ONNX ops
             (with ONNX namespace).
             OperatorExportTypes.ONNX_ATEN: All ops are exported as ATen ops
             (with aten namespace).
             OperatorExportTypes.ONNX_ATEN_FALLBACK: If an ATen op is not supported
             in ONNX or its symbolic is missing, fall back on ATen op. Registered ops
             are exported to ONNX regularly.
             Example graph::

                 graph(%0 : Float)::
                   %3 : int = prim::Constant[value=0]()
                   %4 : Float = aten::triu(%0, %3) # missing op
                   %5 : Float = aten::mul(%4, %0) # registered op
                   return (%5)

             is exported as::

                 graph(%0 : Float)::
                   %1 : Long() = onnx::Constant[value={0}]()
                   %2 : Float = aten::ATen[operator="triu"](%0, %1)  # missing op
                   %3 : Float = onnx::Mul(%2, %0) # registered op
                   return (%3)

             In the above example, aten::triu is not supported in ONNX, hence
             exporter falls back on this op.
             OperatorExportTypes.RAW: Export raw ir.
             OperatorExportTypes.ONNX_FALLTHROUGH: If an op is not supported
             in ONNX, fall through and export the operator as is, as a custom
             ONNX op. Using this mode, the op can be exported and implemented by
             the user for their runtime backend.
             Example graph::

                 graph(%x.1 : Long(1, strides=[1]))::
                   %1 : None = prim::Constant()
                   %2 : Tensor = aten::sum(%x.1, %1)
                   %y.1 : Tensor[] = prim::ListConstruct(%2)
                   return (%y.1)

             is exported as::

                 graph(%x.1 : Long(1, strides=[1]))::
                   %1 : Tensor = onnx::ReduceSum[keepdims=0](%x.1)
                   %y.1 : Long() = prim::ListConstruct(%1)
                   return (%y.1)

             In the above example, prim::ListConstruct is not supported, hence
             exporter falls through.

         opset_version (int, default is 9): by default we export the model to the
             opset version of the onnx submodule. Since ONNX's latest opset may
             evolve before next stable release, by default we export to one stable
             opset version. Right now, supported stable opset version is 9.
             The opset_version must be _onnx_main_opset or in _onnx_stable_opsets
             which are defined in torch/onnx/symbolic_helper.py
         do_constant_folding (bool, default False): If True, the constant-folding
             optimization is applied to the model during export. Constant-folding
             optimization will replace some of the ops that have all constant
             inputs, with pre-computed constant nodes.
         example_outputs (tuple of Tensors, default None): Model's example outputs being exported.
             example_outputs must be provided when exporting a ScriptModule or TorchScript Function.
         strip_doc_string (bool, default True): if True, strips the field
             "doc_string" from the exported model, which information about the stack
             trace.
         dynamic_axes (dict<string, dict<int, string>> or dict<string, list(int)>, default empty dict):
             a dictionary to specify dynamic axes of input/output, such that:
             - KEY:  input and/or output names
             - VALUE: index of dynamic axes for given key and potentially the name to be used for
             exported dynamic axes. In general the value is defined according to one of the following
             ways or a combination of both:
             (1). A list of integers specifying the dynamic axes of provided input. In this scenario
             automated names will be generated and applied to dynamic axes of provided input/output
             during export.
             OR (2). An inner dictionary that specifies a mapping FROM the index of dynamic axis in
             corresponding input/output TO the name that is desired to be applied on such axis of
             such input/output during export.

             Example. if we have the following shape for inputs and outputs:

             .. code-block:: none

                 shape(input_1) = ('b', 3, 'w', 'h')
                 and shape(input_2) = ('b', 4)
                 and shape(output)  = ('b', 'd', 5)

             Then `dynamic axes` can be defined either as:

             1. ONLY INDICES::

                 ``dynamic_axes = {'input_1':[0, 2, 3],
                                   'input_2':[0],
                                   'output':[0, 1]}``
                 where automatic names will be generated for exported dynamic axes

             2. INDICES WITH CORRESPONDING NAMES::

                 ``dynamic_axes = {'input_1':{0:'batch',
                                              1:'width',
                                              2:'height'},
                                   'input_2':{0:'batch'},
                                   'output':{0:'batch',
                                             1:'detections'}}``
                 where provided names will be applied to exported dynamic axes

             3. MIXED MODE OF (1) and (2)::

                 ``dynamic_axes = {'input_1':[0, 2, 3],
                                   'input_2':{0:'batch'},
                                   'output':[0,1]}``

         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 (such as constant folding
             etc.) by backends/runtimes that execute these graphs. If
             unspecified (default None), then the behavior is chosen
             automatically as follows. If operator_export_type is
             OperatorExportTypes.ONNX, the behavior is equivalent to setting
             this argument to False. For other values of operator_export_type,
             the behavior is equivalent to setting this argument to True. Note
             that for ONNX opset version < 9, initializers MUST be part of graph
             inputs. Therefore, if opset_version argument is set to a 8 or
             lower, this argument will be ignored.
         custom_opsets (dict<string, int>, default empty dict): A dictionary to indicate
             custom opset domain and version at export. If model contains a custom opset,
             it is optional to specify the domain and opset version in the dictionary:
             - KEY: opset domain name
             - VALUE: opset version
             If the custom opset is not provided in this dictionary, opset version is set
             to 1 by default.
         enable_onnx_checker (bool, default True): If True the onnx model checker will be run
             as part of the export, to ensure the exported model is a valid ONNX model.
         use_external_data_format (bool, default False): If True, then the model is exported
             in ONNX external data format, in which case some of the model parameters are stored
             in external binary files and not in the ONNX model file itself. See link for format
             details:
             https://github.com/onnx/onnx/blob/8b3f7e2e7a0f2aba0e629e23d89f07c7fc0e6a5e/onnx/onnx.proto#L423
             Also, in this case,  argument 'f' must be a string specifying the location of the model.
             The external binary files will be stored in the same location specified by the model
             location 'f'. If False, then the model is stored in regular format, i.e. model and
             parameters are all in one file. This argument is ignored for all export types other
             than ONNX.
     """

     from torch.onnx import utils
     return utils.export(model, args, f, export_params, verbose, training,
                         input_names, output_names, aten, export_raw_ir,
                         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):
     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.

     In version 1.6 changed to this from set_training
     """

     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"""
     Check whether it's in the middle of the ONNX export.
     This function returns True in the middle of torch.onnx.export().
     torch.onnx.export should be executed with single thread.
     """

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


 def register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version):
     from torch.onnx import utils
     return 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]


	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, aten=False, export_raw_ir=False,
	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"""
	Export a model into ONNX format. This exporter runs your model
	once in order to get a trace of its execution to be exported;
	at the moment, it supports a limited set of dynamic models (e.g., RNNs.)

	Args:
	model (torch.nn.Module): the model to be exported.
	args (tuple of arguments or torch.Tensor, a dictionary consisting of named arguments (optional)):
	a dictionary to specify the input to the corresponding named parameter:
	- KEY: str, named parameter
	- VALUE: corresponding input
	args can be structured either as:

	1. ONLY A TUPLE OF ARGUMENTS or torch.Tensor::

	"args = (x, y, z)"

	The inputs to the model, e.g., 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 args. If args is a Tensor, this is equivalent to having
	called it with a 1-ary tuple of that Tensor.

	2. A TUPLE OF ARGUEMENTS WITH A DICTIONARY OF NAMED PARAMETERS::

	"args = (x,
	{
	'y': input_y,
	'z': input_z
	})"

	The inputs to the model are structured as a tuple consisting of
	non-keyword arguments and the last value of this tuple being a dictionary
	consisting of named parameters and the corresponding inputs as key-value pairs.
	If certain named argument is not present in the dictionary, it is assigned
	the default value, or None if default value is not provided.

	Cases in which an dictionary input is the last input of the args tuple
	would cause a conflict when a dictionary of named parameters is used.
	The model below provides such an example.

	class Model(torch.nn.Module):
	def forward(self, k, x):
	...
	return x

	m = Model()
	k = torch.randn(2, 3)
	x = {torch.tensor(1.): torch.randn(2, 3)}

	In the previous iteration, the call to export API would look like

	torch.onnx.export(model, (k, x), 'test.onnx')

	This would work as intended. However, the export function
	would now assume that the `x` input is intended to represent the optional
	dictionary consisting of named arguments. In order to prevent this from being
	an issue a constraint is placed to provide an empty dictionary as the last
	input in the tuple args in such cases. The new call would look like this.

	torch.onnx.export(model, (k, x, {}), 'test.onnx')

	f: a file-like object (has to implement fileno that returns a file descriptor)
	or a string containing a file name. A binary Protobuf will be written
	to this file.
	export_params (bool, default True): if specified, 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, the ordering as specified by ``model.state_dict().values()``
	verbose (bool, default False): if specified, we will print out a debug
	description of the trace being exported.
	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 to a training friendly mode if model.training is True.
	TrainingMode.TRAINING: export the model in a training friendly mode.
	input_names(list of strings, default empty list): names to assign to the
	input nodes of the graph, in order
	output_names(list of strings, default empty list): names to assign to the
	output nodes of the graph, in order
	aten (bool, default False): [DEPRECATED. use operator_export_type] export the
	model in aten mode. If using aten mode, all the ops original exported
	by the functions in symbolic_opset<version>.py are exported as ATen ops.
	export_raw_ir (bool, default False): [DEPRECATED. use operator_export_type]
	export the internal IR directly instead of converting it to ONNX ops.
	operator_export_type (enum, default OperatorExportTypes.ONNX):
	OperatorExportTypes.ONNX: All ops are exported as regular ONNX ops
	(with ONNX namespace).
	OperatorExportTypes.ONNX_ATEN: All ops are exported as ATen ops
	(with aten namespace).
	OperatorExportTypes.ONNX_ATEN_FALLBACK: If an ATen op is not supported
	in ONNX or its symbolic is missing, fall back on ATen op. Registered ops
	are exported to ONNX regularly.
	Example graph::

	graph(%0 : Float)::
	%3 : int = prim::Constant[value=0]()
	%4 : Float = aten::triu(%0, %3) # missing op
	%5 : Float = aten::mul(%4, %0) # registered op
	return (%5)

	is exported as::

	graph(%0 : Float)::
	%1 : Long() = onnx::Constant[value={0}]()
	%2 : Float = aten::ATen[operator="triu"](%0, %1) # missing op
	%3 : Float = onnx::Mul(%2, %0) # registered op
	return (%3)

	In the above example, aten::triu is not supported in ONNX, hence
	exporter falls back on this op.
	OperatorExportTypes.RAW: Export raw ir.
	OperatorExportTypes.ONNX_FALLTHROUGH: If an op is not supported
	in ONNX, fall through and export the operator as is, as a custom
	ONNX op. Using this mode, the op can be exported and implemented by
	the user for their runtime backend.
	Example graph::

	graph(%x.1 : Long(1, strides=[1]))::
	%1 : None = prim::Constant()
	%2 : Tensor = aten::sum(%x.1, %1)
	%y.1 : Tensor[] = prim::ListConstruct(%2)
	return (%y.1)

	is exported as::

	graph(%x.1 : Long(1, strides=[1]))::
	%1 : Tensor = onnx::ReduceSum[keepdims=0](%x.1)
	%y.1 : Long() = prim::ListConstruct(%1)
	return (%y.1)

	In the above example, prim::ListConstruct is not supported, hence
	exporter falls through.

	opset_version (int, default is 9): by default we export the model to the
	opset version of the onnx submodule. Since ONNX's latest opset may
	evolve before next stable release, by default we export to one stable
	opset version. Right now, supported stable opset version is 9.
	The opset_version must be _onnx_main_opset or in _onnx_stable_opsets
	which are defined in torch/onnx/symbolic_helper.py
	do_constant_folding (bool, default False): If True, the constant-folding
	optimization is applied to the model during export. Constant-folding
	optimization will replace some of the ops that have all constant
	inputs, with pre-computed constant nodes.
	example_outputs (tuple of Tensors, default None): Model's example outputs being exported.
	example_outputs must be provided when exporting a ScriptModule or TorchScript Function.
	strip_doc_string (bool, default True): if True, strips the field
	"doc_string" from the exported model, which information about the stack
	trace.
	dynamic_axes (dict<string, dict<int, string>> or dict<string, list(int)>, default empty dict):
	a dictionary to specify dynamic axes of input/output, such that:
	- KEY: input and/or output names
	- VALUE: index of dynamic axes for given key and potentially the name to be used for
	exported dynamic axes. In general the value is defined according to one of the following
	ways or a combination of both:
	(1). A list of integers specifying the dynamic axes of provided input. In this scenario
	automated names will be generated and applied to dynamic axes of provided input/output
	during export.
	OR (2). An inner dictionary that specifies a mapping FROM the index of dynamic axis in
	corresponding input/output TO the name that is desired to be applied on such axis of
	such input/output during export.

	Example. if we have the following shape for inputs and outputs:

	.. code-block:: none

	shape(input_1) = ('b', 3, 'w', 'h')
	and shape(input_2) = ('b', 4)
	and shape(output) = ('b', 'd', 5)

	Then `dynamic axes` can be defined either as:

	1. ONLY INDICES::

	``dynamic_axes = {'input_1':[0, 2, 3],
	'input_2':[0],
	'output':[0, 1]}``
	where automatic names will be generated for exported dynamic axes

	2. INDICES WITH CORRESPONDING NAMES::

	``dynamic_axes = {'input_1':{0:'batch',
	1:'width',
	2:'height'},
	'input_2':{0:'batch'},
	'output':{0:'batch',
	1:'detections'}}``
	where provided names will be applied to exported dynamic axes

	3. MIXED MODE OF (1) and (2)::

	``dynamic_axes = {'input_1':[0, 2, 3],
	'input_2':{0:'batch'},
	'output':[0,1]}``

	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 (such as constant folding
	etc.) by backends/runtimes that execute these graphs. If
	unspecified (default None), then the behavior is chosen
	automatically as follows. If operator_export_type is
	OperatorExportTypes.ONNX, the behavior is equivalent to setting
	this argument to False. For other values of operator_export_type,
	the behavior is equivalent to setting this argument to True. Note
	that for ONNX opset version < 9, initializers MUST be part of graph
	inputs. Therefore, if opset_version argument is set to a 8 or
	lower, this argument will be ignored.
	custom_opsets (dict<string, int>, default empty dict): A dictionary to indicate
	custom opset domain and version at export. If model contains a custom opset,
	it is optional to specify the domain and opset version in the dictionary:
	- KEY: opset domain name
	- VALUE: opset version
	If the custom opset is not provided in this dictionary, opset version is set
	to 1 by default.
	enable_onnx_checker (bool, default True): If True the onnx model checker will be run
	as part of the export, to ensure the exported model is a valid ONNX model.
	use_external_data_format (bool, default False): If True, then the model is exported
	in ONNX external data format, in which case some of the model parameters are stored
	in external binary files and not in the ONNX model file itself. See link for format
	details:
	https://github.com/onnx/onnx/blob/8b3f7e2e7a0f2aba0e629e23d89f07c7fc0e6a5e/onnx/onnx.proto#L423
	Also, in this case, argument 'f' must be a string specifying the location of the model.
	The external binary files will be stored in the same location specified by the model
	location 'f'. If False, then the model is stored in regular format, i.e. model and
	parameters are all in one file. This argument is ignored for all export types other
	than ONNX.
	"""

	from torch.onnx import utils
	return utils.export(model, args, f, export_params, verbose, training,
	input_names, output_names, aten, export_raw_ir,
	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):
	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.

	In version 1.6 changed to this from set_training
	"""

	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"""
	Check whether it's in the middle of the ONNX export.
	This function returns True in the middle of torch.onnx.export().
	torch.onnx.export should be executed with single thread.
	"""

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


	def register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version):
	from torch.onnx import utils
	return utils.register_custom_op_symbolic(symbolic_name, symbolic_fn, opset_version)