""" @generated by mypy-protobuf. Do not edit manually! isort:skip_file """ import builtins import collections.abc import sys import google.protobuf.any_pb2 import google.protobuf.descriptor import google.protobuf.internal.containers import google.protobuf.message import tensorflow.core.framework.graph_pb2 import tensorflow.core.framework.op_def_pb2 import tensorflow.core.framework.tensor_shape_pb2 import tensorflow.core.framework.types_pb2 import tensorflow.core.protobuf.saved_object_graph_pb2 import tensorflow.core.protobuf.saver_pb2 import tensorflow.core.protobuf.struct_pb2 if sys.version_info >= (3, 8): import typing as typing_extensions else: import typing_extensions DESCRIPTOR: google.protobuf.descriptor.FileDescriptor @typing_extensions.final class MetaGraphDef(google.protobuf.message.Message): """Protocol buffer containing the following which are necessary to restart training, run inference. It can be used to serialize/de-serialize memory objects necessary for running computation in a graph when crossing the process boundary. It can be used for long term storage of graphs, cross-language execution of graphs, etc. MetaInfoDef GraphDef SaverDef CollectionDef TensorInfo SignatureDef """ DESCRIPTOR: google.protobuf.descriptor.Descriptor @typing_extensions.final class MetaInfoDef(google.protobuf.message.Message): """Meta information regarding the graph to be exported. To be used by users of this protocol buffer to encode information regarding their meta graph. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor @typing_extensions.final class FunctionAliasesEntry(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor KEY_FIELD_NUMBER: builtins.int VALUE_FIELD_NUMBER: builtins.int key: builtins.str value: builtins.str def __init__( self, *, key: builtins.str | None = ..., value: builtins.str | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ... META_GRAPH_VERSION_FIELD_NUMBER: builtins.int STRIPPED_OP_LIST_FIELD_NUMBER: builtins.int ANY_INFO_FIELD_NUMBER: builtins.int TAGS_FIELD_NUMBER: builtins.int TENSORFLOW_VERSION_FIELD_NUMBER: builtins.int TENSORFLOW_GIT_VERSION_FIELD_NUMBER: builtins.int STRIPPED_DEFAULT_ATTRS_FIELD_NUMBER: builtins.int FUNCTION_ALIASES_FIELD_NUMBER: builtins.int meta_graph_version: builtins.str """User specified Version string. Can be the name of the model and revision, steps this model has been trained to, etc. """ @property def stripped_op_list(self) -> tensorflow.core.framework.op_def_pb2.OpList: """A copy of the OpDefs used by the producer of this graph_def. Descriptions and Ops not used in graph_def are stripped out. """ @property def any_info(self) -> google.protobuf.any_pb2.Any: """A serialized protobuf. Can be the time this meta graph is created, or modified, or name of the model. """ @property def tags(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]: """User supplied tag(s) on the meta_graph and included graph_def. MetaGraphDefs should be tagged with their capabilities or use-cases. Examples: "train", "serve", "gpu", "tpu", etc. These tags enable loaders to access the MetaGraph(s) appropriate for a specific use-case or runtime environment. """ tensorflow_version: builtins.str """The __version__ string of the tensorflow build used to write this graph. This will be populated by the framework, which will overwrite any user supplied value. """ tensorflow_git_version: builtins.str """The __git_version__ string of the tensorflow build used to write this graph. This will be populated by the framework, which will overwrite any user supplied value. """ stripped_default_attrs: builtins.bool """A flag to denote whether default-valued attrs have been stripped from the nodes in this graph_def. """ @property def function_aliases(self) -> google.protobuf.internal.containers.ScalarMap[builtins.str, builtins.str]: """FunctionDef name to aliases mapping.""" def __init__( self, *, meta_graph_version: builtins.str | None = ..., stripped_op_list: tensorflow.core.framework.op_def_pb2.OpList | None = ..., any_info: google.protobuf.any_pb2.Any | None = ..., tags: collections.abc.Iterable[builtins.str] | None = ..., tensorflow_version: builtins.str | None = ..., tensorflow_git_version: builtins.str | None = ..., stripped_default_attrs: builtins.bool | None = ..., function_aliases: collections.abc.Mapping[builtins.str, builtins.str] | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["any_info", b"any_info", "stripped_op_list", b"stripped_op_list"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["any_info", b"any_info", "function_aliases", b"function_aliases", "meta_graph_version", b"meta_graph_version", "stripped_default_attrs", b"stripped_default_attrs", "stripped_op_list", b"stripped_op_list", "tags", b"tags", "tensorflow_git_version", b"tensorflow_git_version", "tensorflow_version", b"tensorflow_version"]) -> None: ... @typing_extensions.final class CollectionDefEntry(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor KEY_FIELD_NUMBER: builtins.int VALUE_FIELD_NUMBER: builtins.int key: builtins.str @property def value(self) -> global___CollectionDef: ... def __init__( self, *, key: builtins.str | None = ..., value: global___CollectionDef | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ... @typing_extensions.final class SignatureDefEntry(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor KEY_FIELD_NUMBER: builtins.int VALUE_FIELD_NUMBER: builtins.int key: builtins.str @property def value(self) -> global___SignatureDef: ... def __init__( self, *, key: builtins.str | None = ..., value: global___SignatureDef | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ... META_INFO_DEF_FIELD_NUMBER: builtins.int GRAPH_DEF_FIELD_NUMBER: builtins.int SAVER_DEF_FIELD_NUMBER: builtins.int COLLECTION_DEF_FIELD_NUMBER: builtins.int SIGNATURE_DEF_FIELD_NUMBER: builtins.int ASSET_FILE_DEF_FIELD_NUMBER: builtins.int OBJECT_GRAPH_DEF_FIELD_NUMBER: builtins.int @property def meta_info_def(self) -> global___MetaGraphDef.MetaInfoDef: ... @property def graph_def(self) -> tensorflow.core.framework.graph_pb2.GraphDef: """GraphDef.""" @property def saver_def(self) -> tensorflow.core.protobuf.saver_pb2.SaverDef: """SaverDef.""" @property def collection_def(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___CollectionDef]: """collection_def: Map from collection name to collections. See CollectionDef section for details. """ @property def signature_def(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___SignatureDef]: """signature_def: Map from user supplied key for a signature to a single SignatureDef. """ @property def asset_file_def(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___AssetFileDef]: """Asset file def to be used with the defined graph.""" @property def object_graph_def(self) -> tensorflow.core.protobuf.saved_object_graph_pb2.SavedObjectGraph: """Extra information about the structure of functions and stateful objects.""" def __init__( self, *, meta_info_def: global___MetaGraphDef.MetaInfoDef | None = ..., graph_def: tensorflow.core.framework.graph_pb2.GraphDef | None = ..., saver_def: tensorflow.core.protobuf.saver_pb2.SaverDef | None = ..., collection_def: collections.abc.Mapping[builtins.str, global___CollectionDef] | None = ..., signature_def: collections.abc.Mapping[builtins.str, global___SignatureDef] | None = ..., asset_file_def: collections.abc.Iterable[global___AssetFileDef] | None = ..., object_graph_def: tensorflow.core.protobuf.saved_object_graph_pb2.SavedObjectGraph | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["graph_def", b"graph_def", "meta_info_def", b"meta_info_def", "object_graph_def", b"object_graph_def", "saver_def", b"saver_def"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["asset_file_def", b"asset_file_def", "collection_def", b"collection_def", "graph_def", b"graph_def", "meta_info_def", b"meta_info_def", "object_graph_def", b"object_graph_def", "saver_def", b"saver_def", "signature_def", b"signature_def"]) -> None: ... global___MetaGraphDef = MetaGraphDef @typing_extensions.final class CollectionDef(google.protobuf.message.Message): """CollectionDef should cover most collections. To add a user-defined collection, do one of the following: 1. For simple data types, such as string, int, float: tf.add_to_collection("your_collection_name", your_simple_value) strings will be stored as bytes_list. 2. For Protobuf types, there are three ways to add them: 1) tf.add_to_collection("your_collection_name", your_proto.SerializeToString()) collection_def { key: "user_defined_bytes_collection" value { bytes_list { value: "queue_name: \\"test_queue\\"\\n" } } } or 2) tf.add_to_collection("your_collection_name", str(your_proto)) collection_def { key: "user_defined_string_collection" value { bytes_list { value: "\\n\\ntest_queue" } } } or 3) any_buf = any_pb2.Any() tf.add_to_collection("your_collection_name", any_buf.Pack(your_proto)) collection_def { key: "user_defined_any_collection" value { any_list { value { type_url: "type.googleapis.com/tensorflow.QueueRunnerDef" value: "\\n\\ntest_queue" } } } } 3. For Python objects, implement to_proto() and from_proto(), and register them in the following manner: ops.register_proto_function("your_collection_name", proto_type, to_proto=YourPythonObject.to_proto, from_proto=YourPythonObject.from_proto) These functions will be invoked to serialize and de-serialize the collection. For example, ops.register_proto_function(ops.GraphKeys.GLOBAL_VARIABLES, proto_type=variable_pb2.VariableDef, to_proto=Variable.to_proto, from_proto=Variable.from_proto) """ DESCRIPTOR: google.protobuf.descriptor.Descriptor @typing_extensions.final class NodeList(google.protobuf.message.Message): """NodeList is used for collecting nodes in graph. For example collection_def { key: "summaries" value { node_list { value: "input_producer/ScalarSummary:0" value: "shuffle_batch/ScalarSummary:0" value: "ImageSummary:0" } } """ DESCRIPTOR: google.protobuf.descriptor.Descriptor VALUE_FIELD_NUMBER: builtins.int @property def value(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]: ... def __init__( self, *, value: collections.abc.Iterable[builtins.str] | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ... @typing_extensions.final class BytesList(google.protobuf.message.Message): """BytesList is used for collecting strings and serialized protobufs. For example: collection_def { key: "trainable_variables" value { bytes_list { value: "\\n\\017conv1/weights:0\\022\\024conv1/weights/Assign \\032\\024conv1/weights/read:0" value: "\\n\\016conv1/biases:0\\022\\023conv1/biases/Assign\\032 \\023conv1/biases/read:0" } } } """ DESCRIPTOR: google.protobuf.descriptor.Descriptor VALUE_FIELD_NUMBER: builtins.int @property def value(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bytes]: ... def __init__( self, *, value: collections.abc.Iterable[builtins.bytes] | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ... @typing_extensions.final class Int64List(google.protobuf.message.Message): """Int64List is used for collecting int, int64 and long values.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor VALUE_FIELD_NUMBER: builtins.int @property def value(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: ... def __init__( self, *, value: collections.abc.Iterable[builtins.int] | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ... @typing_extensions.final class FloatList(google.protobuf.message.Message): """FloatList is used for collecting float values.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor VALUE_FIELD_NUMBER: builtins.int @property def value(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: ... def __init__( self, *, value: collections.abc.Iterable[builtins.float] | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ... @typing_extensions.final class AnyList(google.protobuf.message.Message): """AnyList is used for collecting Any protos.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor VALUE_FIELD_NUMBER: builtins.int @property def value(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[google.protobuf.any_pb2.Any]: ... def __init__( self, *, value: collections.abc.Iterable[google.protobuf.any_pb2.Any] | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ... NODE_LIST_FIELD_NUMBER: builtins.int BYTES_LIST_FIELD_NUMBER: builtins.int INT64_LIST_FIELD_NUMBER: builtins.int FLOAT_LIST_FIELD_NUMBER: builtins.int ANY_LIST_FIELD_NUMBER: builtins.int @property def node_list(self) -> global___CollectionDef.NodeList: ... @property def bytes_list(self) -> global___CollectionDef.BytesList: ... @property def int64_list(self) -> global___CollectionDef.Int64List: ... @property def float_list(self) -> global___CollectionDef.FloatList: ... @property def any_list(self) -> global___CollectionDef.AnyList: ... def __init__( self, *, node_list: global___CollectionDef.NodeList | None = ..., bytes_list: global___CollectionDef.BytesList | None = ..., int64_list: global___CollectionDef.Int64List | None = ..., float_list: global___CollectionDef.FloatList | None = ..., any_list: global___CollectionDef.AnyList | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["any_list", b"any_list", "bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind", "node_list", b"node_list"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["any_list", b"any_list", "bytes_list", b"bytes_list", "float_list", b"float_list", "int64_list", b"int64_list", "kind", b"kind", "node_list", b"node_list"]) -> None: ... def WhichOneof(self, oneof_group: typing_extensions.Literal["kind", b"kind"]) -> typing_extensions.Literal["node_list", "bytes_list", "int64_list", "float_list", "any_list"] | None: ... global___CollectionDef = CollectionDef @typing_extensions.final class TensorInfo(google.protobuf.message.Message): """Information about a Tensor necessary for feeding or retrieval.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor @typing_extensions.final class CooSparse(google.protobuf.message.Message): """For sparse tensors, The COO encoding stores a triple of values, indices, and shape. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor VALUES_TENSOR_NAME_FIELD_NUMBER: builtins.int INDICES_TENSOR_NAME_FIELD_NUMBER: builtins.int DENSE_SHAPE_TENSOR_NAME_FIELD_NUMBER: builtins.int values_tensor_name: builtins.str """The shape of the values Tensor is [?]. Its dtype must be the dtype of the SparseTensor as a whole, given in the enclosing TensorInfo. """ indices_tensor_name: builtins.str """The indices Tensor must have dtype int64 and shape [?, ?].""" dense_shape_tensor_name: builtins.str """The dynamic logical shape represented by the SparseTensor is recorded in the Tensor referenced here. It must have dtype int64 and shape [?]. """ def __init__( self, *, values_tensor_name: builtins.str | None = ..., indices_tensor_name: builtins.str | None = ..., dense_shape_tensor_name: builtins.str | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["dense_shape_tensor_name", b"dense_shape_tensor_name", "indices_tensor_name", b"indices_tensor_name", "values_tensor_name", b"values_tensor_name"]) -> None: ... @typing_extensions.final class CompositeTensor(google.protobuf.message.Message): """Generic encoding for composite tensors.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor TYPE_SPEC_FIELD_NUMBER: builtins.int COMPONENTS_FIELD_NUMBER: builtins.int @property def type_spec(self) -> tensorflow.core.protobuf.struct_pb2.TypeSpecProto: """The serialized TypeSpec for the composite tensor.""" @property def components(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorInfo]: """A TensorInfo for each flattened component tensor.""" def __init__( self, *, type_spec: tensorflow.core.protobuf.struct_pb2.TypeSpecProto | None = ..., components: collections.abc.Iterable[global___TensorInfo] | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["type_spec", b"type_spec"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["components", b"components", "type_spec", b"type_spec"]) -> None: ... NAME_FIELD_NUMBER: builtins.int COO_SPARSE_FIELD_NUMBER: builtins.int COMPOSITE_TENSOR_FIELD_NUMBER: builtins.int DTYPE_FIELD_NUMBER: builtins.int TENSOR_SHAPE_FIELD_NUMBER: builtins.int name: builtins.str """For dense `Tensor`s, the name of the tensor in the graph.""" @property def coo_sparse(self) -> global___TensorInfo.CooSparse: """There are many possible encodings of sparse matrices (https://en.wikipedia.org/wiki/Sparse_matrix). Currently, TensorFlow uses only the COO encoding. This is supported and documented in the SparseTensor Python class. """ @property def composite_tensor(self) -> global___TensorInfo.CompositeTensor: """Generic encoding for CompositeTensors.""" dtype: tensorflow.core.framework.types_pb2.DataType.ValueType @property def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: """The static shape should be recorded here, to the extent that it can be known in advance. In the case of a SparseTensor, this field describes the logical shape of the represented tensor (aka dense_shape). """ def __init__( self, *, name: builtins.str | None = ..., coo_sparse: global___TensorInfo.CooSparse | None = ..., composite_tensor: global___TensorInfo.CompositeTensor | None = ..., dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ..., tensor_shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["composite_tensor", b"composite_tensor", "coo_sparse", b"coo_sparse", "encoding", b"encoding", "name", b"name", "tensor_shape", b"tensor_shape"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["composite_tensor", b"composite_tensor", "coo_sparse", b"coo_sparse", "dtype", b"dtype", "encoding", b"encoding", "name", b"name", "tensor_shape", b"tensor_shape"]) -> None: ... def WhichOneof(self, oneof_group: typing_extensions.Literal["encoding", b"encoding"]) -> typing_extensions.Literal["name", "coo_sparse", "composite_tensor"] | None: ... global___TensorInfo = TensorInfo @typing_extensions.final class SignatureDef(google.protobuf.message.Message): """SignatureDef defines the signature of a computation supported by a TensorFlow graph. For example, a model with two loss computations, sharing a single input, might have the following signature_def map, in a MetaGraphDef message. Note that across the two SignatureDefs "loss_A" and "loss_B", the input key, output key, and method_name are identical, and will be used by system(s) that implement or rely upon this particular loss method. The output tensor names differ, demonstrating how different outputs can exist for the same method. signature_def { key: "loss_A" value { inputs { key: "input" value { name: "input:0" dtype: DT_STRING tensor_shape: ... } } outputs { key: "loss_output" value { name: "loss_output_A:0" dtype: DT_FLOAT tensor_shape: ... } } method_name: "some/package/compute_loss" } ... } signature_def { key: "loss_B" value { inputs { key: "input" value { name: "input:0" dtype: DT_STRING tensor_shape: ... } } outputs { key: "loss_output" value { name: "loss_output_B:0" dtype: DT_FLOAT tensor_shape: ... } } method_name: "some/package/compute_loss" } ... } """ DESCRIPTOR: google.protobuf.descriptor.Descriptor @typing_extensions.final class InputsEntry(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor KEY_FIELD_NUMBER: builtins.int VALUE_FIELD_NUMBER: builtins.int key: builtins.str @property def value(self) -> global___TensorInfo: ... def __init__( self, *, key: builtins.str | None = ..., value: global___TensorInfo | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ... @typing_extensions.final class OutputsEntry(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor KEY_FIELD_NUMBER: builtins.int VALUE_FIELD_NUMBER: builtins.int key: builtins.str @property def value(self) -> global___TensorInfo: ... def __init__( self, *, key: builtins.str | None = ..., value: global___TensorInfo | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ... INPUTS_FIELD_NUMBER: builtins.int OUTPUTS_FIELD_NUMBER: builtins.int METHOD_NAME_FIELD_NUMBER: builtins.int @property def inputs(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___TensorInfo]: """Named input parameters.""" @property def outputs(self) -> google.protobuf.internal.containers.MessageMap[builtins.str, global___TensorInfo]: """Named output parameters.""" method_name: builtins.str """Extensible method_name information enabling third-party users to mark a SignatureDef as supporting a particular method. This enables producers and consumers of SignatureDefs, e.g. a model definition library and a serving library to have a clear hand-off regarding the semantics of a computation. Note that multiple SignatureDefs in a single MetaGraphDef may have the same method_name. This is commonly used to support multi-headed computation, where a single graph computation may return multiple results. """ def __init__( self, *, inputs: collections.abc.Mapping[builtins.str, global___TensorInfo] | None = ..., outputs: collections.abc.Mapping[builtins.str, global___TensorInfo] | None = ..., method_name: builtins.str | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["inputs", b"inputs", "method_name", b"method_name", "outputs", b"outputs"]) -> None: ... global___SignatureDef = SignatureDef @typing_extensions.final class AssetFileDef(google.protobuf.message.Message): """An asset file def for a single file or a set of sharded files with the same name. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor TENSOR_INFO_FIELD_NUMBER: builtins.int FILENAME_FIELD_NUMBER: builtins.int @property def tensor_info(self) -> global___TensorInfo: """The tensor to bind the asset filename to.""" filename: builtins.str """The filename within an assets directory. Note: does not include the path prefix, i.e. directories. For an asset at /tmp/path/vocab.txt, the filename would be "vocab.txt". """ def __init__( self, *, tensor_info: global___TensorInfo | None = ..., filename: builtins.str | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["tensor_info", b"tensor_info"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["filename", b"filename", "tensor_info", b"tensor_info"]) -> None: ... global___AssetFileDef = AssetFileDef