""" @generated by mypy-protobuf. Do not edit manually! isort:skip_file """ import builtins import collections.abc import sys import google.protobuf.descriptor import google.protobuf.internal.containers import google.protobuf.message import tensorflow.core.framework.resource_handle_pb2 import tensorflow.core.framework.tensor_shape_pb2 import tensorflow.core.framework.types_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 TensorProto(google.protobuf.message.Message): """Protocol buffer representing a tensor.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor DTYPE_FIELD_NUMBER: builtins.int TENSOR_SHAPE_FIELD_NUMBER: builtins.int VERSION_NUMBER_FIELD_NUMBER: builtins.int TENSOR_CONTENT_FIELD_NUMBER: builtins.int HALF_VAL_FIELD_NUMBER: builtins.int FLOAT_VAL_FIELD_NUMBER: builtins.int DOUBLE_VAL_FIELD_NUMBER: builtins.int INT_VAL_FIELD_NUMBER: builtins.int STRING_VAL_FIELD_NUMBER: builtins.int SCOMPLEX_VAL_FIELD_NUMBER: builtins.int INT64_VAL_FIELD_NUMBER: builtins.int BOOL_VAL_FIELD_NUMBER: builtins.int DCOMPLEX_VAL_FIELD_NUMBER: builtins.int RESOURCE_HANDLE_VAL_FIELD_NUMBER: builtins.int VARIANT_VAL_FIELD_NUMBER: builtins.int UINT32_VAL_FIELD_NUMBER: builtins.int UINT64_VAL_FIELD_NUMBER: builtins.int FLOAT8_VAL_FIELD_NUMBER: builtins.int dtype: tensorflow.core.framework.types_pb2.DataType.ValueType @property def tensor_shape(self) -> tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto: """Shape of the tensor. TODO(touts): sort out the 0-rank issues.""" version_number: builtins.int """Only one of the representations below is set, one of "tensor_contents" and the "xxx_val" attributes. We are not using oneof because as oneofs cannot contain repeated fields it would require another extra set of messages. Version number. In version 0, if the "repeated xxx" representations contain only one element, that element is repeated to fill the shape. This makes it easy to represent a constant Tensor with a single value. """ tensor_content: builtins.bytes """Serialized raw tensor content from either Tensor::AsProtoTensorContent or memcpy in tensorflow::grpc::EncodeTensorToByteBuffer. This representation can be used for all tensor types. The purpose of this representation is to reduce serialization overhead during RPC call by avoiding serialization of many repeated small items. """ @property def half_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: """Type specific representations that make it easy to create tensor protos in all languages. Only the representation corresponding to "dtype" can be set. The values hold the flattened representation of the tensor in row major order. DT_HALF, DT_BFLOAT16. Note that since protobuf has no int16 type, we'll have some pointless zero padding for each value here. """ @property def float_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: """DT_FLOAT.""" @property def double_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: """DT_DOUBLE.""" @property def int_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: """DT_INT32, DT_INT16, DT_UINT16, DT_INT8, DT_UINT8.""" @property def string_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bytes]: """DT_STRING""" @property def scomplex_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: """DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real and imaginary parts of i-th single precision complex. """ @property def int64_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: """DT_INT64""" @property def bool_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.bool]: """DT_BOOL""" @property def dcomplex_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.float]: """DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real and imaginary parts of i-th double precision complex. """ @property def resource_handle_val(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[tensorflow.core.framework.resource_handle_pb2.ResourceHandleProto]: """DT_RESOURCE""" @property def variant_val(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___VariantTensorDataProto]: """DT_VARIANT""" @property def uint32_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: """DT_UINT32""" @property def uint64_val(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]: """DT_UINT64""" float8_val: builtins.bytes """DT_FLOAT8_*, use variable-sized set of bytes (i.e. the equivalent of repeated uint8, if such a thing existed). """ def __init__( self, *, dtype: tensorflow.core.framework.types_pb2.DataType.ValueType | None = ..., tensor_shape: tensorflow.core.framework.tensor_shape_pb2.TensorShapeProto | None = ..., version_number: builtins.int | None = ..., tensor_content: builtins.bytes | None = ..., half_val: collections.abc.Iterable[builtins.int] | None = ..., float_val: collections.abc.Iterable[builtins.float] | None = ..., double_val: collections.abc.Iterable[builtins.float] | None = ..., int_val: collections.abc.Iterable[builtins.int] | None = ..., string_val: collections.abc.Iterable[builtins.bytes] | None = ..., scomplex_val: collections.abc.Iterable[builtins.float] | None = ..., int64_val: collections.abc.Iterable[builtins.int] | None = ..., bool_val: collections.abc.Iterable[builtins.bool] | None = ..., dcomplex_val: collections.abc.Iterable[builtins.float] | None = ..., resource_handle_val: collections.abc.Iterable[tensorflow.core.framework.resource_handle_pb2.ResourceHandleProto] | None = ..., variant_val: collections.abc.Iterable[global___VariantTensorDataProto] | None = ..., uint32_val: collections.abc.Iterable[builtins.int] | None = ..., uint64_val: collections.abc.Iterable[builtins.int] | None = ..., float8_val: builtins.bytes | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["tensor_shape", b"tensor_shape"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["bool_val", b"bool_val", "dcomplex_val", b"dcomplex_val", "double_val", b"double_val", "dtype", b"dtype", "float8_val", b"float8_val", "float_val", b"float_val", "half_val", b"half_val", "int64_val", b"int64_val", "int_val", b"int_val", "resource_handle_val", b"resource_handle_val", "scomplex_val", b"scomplex_val", "string_val", b"string_val", "tensor_content", b"tensor_content", "tensor_shape", b"tensor_shape", "uint32_val", b"uint32_val", "uint64_val", b"uint64_val", "variant_val", b"variant_val", "version_number", b"version_number"]) -> None: ... global___TensorProto = TensorProto @typing_extensions.final class VariantTensorDataProto(google.protobuf.message.Message): """Protocol buffer representing the serialization format of DT_VARIANT tensors.""" DESCRIPTOR: google.protobuf.descriptor.Descriptor TYPE_NAME_FIELD_NUMBER: builtins.int METADATA_FIELD_NUMBER: builtins.int TENSORS_FIELD_NUMBER: builtins.int type_name: builtins.str """Name of the type of objects being serialized.""" metadata: builtins.bytes """Portions of the object that are not Tensors.""" @property def tensors(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorProto]: """Tensors contained within objects being serialized.""" def __init__( self, *, type_name: builtins.str | None = ..., metadata: builtins.bytes | None = ..., tensors: collections.abc.Iterable[global___TensorProto] | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["metadata", b"metadata", "tensors", b"tensors", "type_name", b"type_name"]) -> None: ... global___VariantTensorDataProto = VariantTensorDataProto