TensorFlow.NET/src/TensorFlowNET.Core/Protobuf/FullType.cs at master · hartoto2/TensorFlow.NET · GitHub

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// <auto-generated>

// Generated by the protocol buffer compiler. DO NOT EDIT!

// source: tensorflow/core/framework/full_type.proto

// </auto-generated>

#pragma warning disable 1591, 0612, 3021, 8981

#region Designer generated code

using pb = global::Google.Protobuf;

using pbc = global::Google.Protobuf.Collections;

using pbr = global::Google.Protobuf.Reflection;

using scg = global::System.Collections.Generic;

namespace Tensorflow {

/// <summary>Holder for reflection information generated from tensorflow/core/framework/full_type.proto</summary>

public static partial class FullTypeReflection {

#region Descriptor

/// <summary>File descriptor for tensorflow/core/framework/full_type.proto</summary>

public static pbr::FileDescriptor Descriptor {

get { return descriptor; }

}

private static pbr::FileDescriptor descriptor;

static FullTypeReflection() {

byte[] descriptorData = global::System.Convert.FromBase64String(

string.Concat(

"Cil0ZW5zb3JmbG93L2NvcmUvZnJhbWV3b3JrL2Z1bGxfdHlwZS5wcm90bxIK",

"dGVuc29yZmxvdyJ/CgtGdWxsVHlwZURlZhInCgd0eXBlX2lkGAEgASgOMhYu",

"dGVuc29yZmxvdy5GdWxsVHlwZUlkEiUKBGFyZ3MYAiADKAsyFy50ZW5zb3Jm",

"bG93LkZ1bGxUeXBlRGVmEgsKAXMYAyABKAlIABILCgFpGAQgASgDSABCBgoE",

"YXR0cirDBAoKRnVsbFR5cGVJZBINCglURlRfVU5TRVQQABILCgdURlRfVkFS",

"EAESCwoHVEZUX0FOWRACEg8KC1RGVF9QUk9EVUNUEAMSDQoJVEZUX05BTUVE",

"EAQSEAoMVEZUX0ZPUl9FQUNIEBQSEAoMVEZUX0NBTExBQkxFEGQSDwoKVEZU",

"X1RFTlNPUhDoBxIOCglURlRfQVJSQVkQ6QcSEQoMVEZUX09QVElPTkFMEOoH",

"EhAKC1RGVF9MSVRFUkFMEOsHEhAKC1RGVF9FTkNPREVEEOwHEg0KCFRGVF9C",

"T09MEMgBEg4KCVRGVF9VSU5UOBDJARIPCgpURlRfVUlOVDE2EMoBEg8KClRG",

"VF9VSU5UMzIQywESDwoKVEZUX1VJTlQ2NBDMARINCghURlRfSU5UOBDNARIO",

"CglURlRfSU5UMTYQzgESDgoJVEZUX0lOVDMyEM8BEg4KCVRGVF9JTlQ2NBDQ",

"ARINCghURlRfSEFMRhDRARIOCglURlRfRkxPQVQQ0gESDwoKVEZUX0RPVUJM",

"RRDTARIRCgxURlRfQkZMT0FUMTYQ1wESEgoNVEZUX0NPTVBMRVg2NBDUARIT",

"Cg5URlRfQ09NUExFWDEyOBDVARIPCgpURlRfU1RSSU5HENYBEhAKC1RGVF9E",

"QVRBU0VUEPZOEg8KClRGVF9SQUdHRUQQ904SEQoMVEZUX0lURVJBVE9SEPhO",

"EhMKDlRGVF9NVVRFWF9MT0NLENpPEhcKElRGVF9MRUdBQ1lfVkFSSUFOVBDb",

"T0KBAQoYb3JnLnRlbnNvcmZsb3cuZnJhbWV3b3JrQg5GdWxsVHlwZVByb3Rv",

"c1ABWlBnaXRodWIuY29tL3RlbnNvcmZsb3cvdGVuc29yZmxvdy90ZW5zb3Jm",

"bG93L2dvL2NvcmUvZnJhbWV3b3JrL2Z1bGxfdHlwZV9nb19wcm90b/gBAWIG",

"cHJvdG8z"));

descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData,

new pbr::FileDescriptor[] { },

new pbr::GeneratedClrTypeInfo(new[] {typeof(global::Tensorflow.FullTypeId), }, null, new pbr::GeneratedClrTypeInfo[] {

new pbr::GeneratedClrTypeInfo(typeof(global::Tensorflow.FullTypeDef), global::Tensorflow.FullTypeDef.Parser, new[]{ "TypeId", "Args", "S", "I" }, new[]{ "Attr" }, null, null, null)

}));

}

#endregion

}

#region Enums

/// <summary>

/// LINT.IfChange

/// Experimental. Represents the complete type information of a TensorFlow value.

/// </summary>

public enum FullTypeId {

/// <summary>

/// The default represents an uninitialized values.

/// </summary>

[pbr::OriginalName("TFT_UNSET")] TftUnset = 0,

/// <summary>

/// Type variables may serve as placeholder for any other type ID in type

/// templates.

///

/// Examples:

/// TFT_DATASET[TFT_VAR["T"]] is a Dataset returning a type indicated by "T".

/// TFT_TENSOR[TFT_VAR["T"]] is a Tensor of n element type indicated by "T".

/// TFT_TENSOR[TFT_VAR["T"]], TFT_TENSOR[TFT_VAR["T"]] are two tensors of

/// identical element types.

/// TFT_TENSOR[TFT_VAR["P"]], TFT_TENSOR[TFT_VAR["Q"]] are two tensors of

/// independent element types.

/// </summary>

[pbr::OriginalName("TFT_VAR")] TftVar = 1,

/// <summary>

/// Wildcard type. Describes a parameter of unknown type. In TensorFlow, that

/// can mean either a "Top" type (accepts any type), or a dynamically typed

/// object whose type is unknown in context.

/// Important: "unknown" does not necessarily mean undeterminable!

/// </summary>

[pbr::OriginalName("TFT_ANY")] TftAny = 2,

/// <summary>

/// The algebraic product type. This is an algebraic type that may be used just

/// for logical grouping. Not to confused with TFT_TUPLE which describes a

/// concrete object of several elements.

///

/// Example:

/// TFT_DATASET[TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT64]]]

/// is a Dataset producing two tensors, an integer one and a float one.

/// </summary>

[pbr::OriginalName("TFT_PRODUCT")] TftProduct = 3,

/// <summary>

/// Represents a named field, with the name stored in the attribute.

///

/// Parametrization:

/// TFT_NAMED[<type>]{<name>}

/// * <type> is the type of the field

/// * <name> is the field name, as string (thpugh can theoretically be an int

/// as well)

///

/// Example:

/// TFT_RECORD[

/// TFT_NAMED[TFT_TENSOR[TFT_INT32]]{'foo'},

/// TFT_NAMED[TFT_TENSOR[TFT_FLOAT32]]{'bar'},

/// ]

/// is a structure with two fields, an int tensor "foo" and a float tensor

/// "bar".

/// </summary>

[pbr::OriginalName("TFT_NAMED")] TftNamed = 4,

/// <summary>

/// Template definition. Expands the variables by repeating a template as

/// arguments of container.

///

/// Parametrization:

/// TFT_FOR_EACH[<container_type>, <template>, <expansions>]

/// * <container_type> is the type of the container that the template will be

/// expanded into

/// * <template> is any type definition that potentially contains type

/// variables

/// * <expansions> is a TFT_VAR and may include more types in the future

///

/// Example:

/// TFT_FOR_EACH[

/// TFT_PRODUCT,

/// TFT_TENSOR[TFT_VAR["t"]],

/// TFT_VAR["t"]

/// ]

/// will substitute a T = TFT_INT32 to TFT_PRODUCT[TFT_TENSOR[TFT_INT32]]

/// and a T = (TFT_INT32, TFT_INT64) to

/// TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_INT64]].

/// </summary>

[pbr::OriginalName("TFT_FOR_EACH")] TftForEach = 20,

/// <summary>

/// Callable types describe functions and ops.

///

/// Parametrization:

/// TFT_CALLABLE[<arg type>, <return type>]

/// * <arg type> is the type of the arguments; TFT_PRODUCT represents

/// multiple

/// arguments.

/// * <return type> is the return type; TFT_PRODUCT represents multiple

/// return values (that means that callables returning multiple things

/// don't necessarily return a single tuple).

///

/// Example:

/// TFT_CALLABLE[

/// TFT_ANY,

/// TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT64]],

/// ]

/// is a callable with unspecified (for now) input arguments, and

/// two return values of type tensor.

/// </summary>

[pbr::OriginalName("TFT_CALLABLE")] TftCallable = 100,

/// <summary>

/// The usual Tensor. This is a parametric type.

///

/// Parametrization:

/// TFT_TENSOR[<element type>, <shape type>]

/// * <element type> is currently limited to one of the element types

/// defined below.

/// * <shape type> is not yet defined, and may only be TFT_UNKNOWN for now.

///

/// A TFT_SHAPE type will be defined in the future.

///

/// Example:

/// TFT_TENSOR[TFT_INT32, TFT_UNKNOWN]

/// is a Tensor of int32 element type and unknown shape.

///

/// TODO(mdan): Define TFT_SHAPE and add more examples.

/// </summary>

[pbr::OriginalName("TFT_TENSOR")] TftTensor = 1000,

/// <summary>

/// Array (or tensorflow::TensorList in the variant type registry).

/// Note: this is not to be confused with the deprecated `TensorArray*` ops

/// which are not supported by FullType.

/// This type represents a random-access list whose elements can be

/// described by a single type. Although immutable, Array is expected to

/// support efficient mutation semantics (i.e. element update) in the

/// user-facing API.

/// The element type may be generic or even TFT_ANY for a heterogenous list.

///

/// Parametrization:

/// TFT_ARRAY[<element type>]

/// * <element type> may be any concrete type.

///

/// Examples:

/// TFT_ARRAY[TFT_TENSOR[TFT_INT32]] is a TensorArray holding int32 Tensors

/// of any shape.

/// TFT_ARRAY[TFT_TENSOR[TFT_UNKNOWN]] is a TensorArray holding Tensors of

/// mixed element types.

/// TFT_ARRAY[TFT_UNKNOWN] is a TensorArray holding any element type.

/// TFT_ARRAY[] is equivalent to TFT_ARRAY[TFT_UNKNOWN].

/// TFT_ARRAY[TFT_ARRAY[]] is an array or arrays (of unknown types).

/// </summary>

[pbr::OriginalName("TFT_ARRAY")] TftArray = 1001,

/// <summary>

/// Optional (or tensorflow::OptionalVariant in the variant type registry).

/// This type represents a value that may either hold an element of a single

/// specified type, or nothing at all.

///

/// Parametrization:

/// TFT_OPTIONAL[<element type>]

/// * <element type> may be any concrete type.

///

/// Examples:

/// TFT_OPTIONAL[TFT_TENSOR[TFT_INT32]] is an Optional holding an int32

/// Tensor of any shape.

/// </summary>

[pbr::OriginalName("TFT_OPTIONAL")] TftOptional = 1002,

/// <summary>

/// Literal types describe compile-time constant values.

/// Literal types may also participate in dependent types.

///

/// Parametrization:

/// TFT_LITERAL[<value type>]{<value>}

/// * <value type> may be any concrete type compatible that can hold <value>

/// * <value> is the type's attribute, and holds the actual literal value

///

/// Examples:

/// TFT_LITERAL[TFT_INT32]{1} is the compile-time constant 1.

/// </summary>

[pbr::OriginalName("TFT_LITERAL")] TftLiteral = 1003,

/// <summary>

/// Encoding types describe a value of a certain type, encoded as a different

/// type.

///

/// Parametrization:

/// TFT_ENCODED[<encoded type>, <encoding type>]

/// * <encoded type> may be any type

/// * <encoding type> may be any type

///

/// Examples:

/// TFT_ENCODING[TFT_INT32, TFT_STRING] is an integer encoded as string.

/// </summary>

[pbr::OriginalName("TFT_ENCODED")] TftEncoded = 1004,

/// <summary>

/// The bool element type.

/// TODO(mdan): Quantized types, legacy representations (e.g. ref)

/// </summary>

[pbr::OriginalName("TFT_BOOL")] TftBool = 200,

/// <summary>

/// Integer element types.

/// </summary>

[pbr::OriginalName("TFT_UINT8")] TftUint8 = 201,

[pbr::OriginalName("TFT_UINT16")] TftUint16 = 202,

[pbr::OriginalName("TFT_UINT32")] TftUint32 = 203,

[pbr::OriginalName("TFT_UINT64")] TftUint64 = 204,

[pbr::OriginalName("TFT_INT8")] TftInt8 = 205,

[pbr::OriginalName("TFT_INT16")] TftInt16 = 206,

[pbr::OriginalName("TFT_INT32")] TftInt32 = 207,

[pbr::OriginalName("TFT_INT64")] TftInt64 = 208,

/// <summary>

/// Floating-point element types.

/// </summary>

[pbr::OriginalName("TFT_HALF")] TftHalf = 209,

[pbr::OriginalName("TFT_FLOAT")] TftFloat = 210,

[pbr::OriginalName("TFT_DOUBLE")] TftDouble = 211,

[pbr::OriginalName("TFT_BFLOAT16")] TftBfloat16 = 215,

/// <summary>

/// Complex element types.

/// TODO(mdan): Represent as TFT_COMPLEX[TFT_DOUBLE] instead?

/// </summary>

[pbr::OriginalName("TFT_COMPLEX64")] TftComplex64 = 212,

[pbr::OriginalName("TFT_COMPLEX128")] TftComplex128 = 213,

/// <summary>

/// The string element type.

/// </summary>

[pbr::OriginalName("TFT_STRING")] TftString = 214,

/// <summary>

/// Datasets created by tf.data ops and APIs. Datasets have generator/iterable

/// semantics, that is, one can construct an iterator from them. Like

/// Array, they are considered to return elements that can be described

/// by a single type. Unlike Array, they do not support random access or

/// mutation, and can potentially produce an infinite number of elements.

/// A datasets can produce logical structures (e.g. multiple elements). This

/// is expressed using TFT_PRODUCT.

///

/// Parametrization: TFT_DATASET[<element type>].

/// * <element type> may be a concrete type or a type symbol. It represents

/// the data type of the elements produced by the dataset.

///

/// Examples:

/// TFT_DATSET[TFT_TENSOR[TFT_INT32]] is a Dataset producing single int32

/// Tensors of unknown shape.

/// TFT_DATSET[TFT_PRODUCT[TFT_TENSOR[TFT_INT32], TFT_TENSOR[TFT_FLOAT32]] is

/// a Dataset producing pairs of Tensors, one integer and one float.

/// Note: The high ID number is to prepare for the eventuality that Datasets

/// will be supported by user types in the future.

/// </summary>

[pbr::OriginalName("TFT_DATASET")] TftDataset = 10102,

/// <summary>

/// A ragged tensor created by tf.ragged ops and APIs.

///

/// Parametrization: TFT_RAGGED[<element_type>].

/// </summary>

[pbr::OriginalName("TFT_RAGGED")] TftRagged = 10103,

/// <summary>

/// Iterators created by tf.data ops and APIs. Very similar to Datasets, except

/// they are mutable.

///

/// Parametrization: TFT_ITERATOR[<element type>].

/// * <element type> may be a concrete type or a type symbol. It represents

/// the data type of the elements produced by the dataset.

/// </summary>

[pbr::OriginalName("TFT_ITERATOR")] TftIterator = 10104,

/// <summary>

/// A mutex lock tensor, produced by tf.raw_ops.MutexLock.

/// Unlike strict execution models, where ownership of a lock is denoted by

/// "running after the lock has been acquired", in non-strict mode, lock

/// ownership is in the true sense: "the op argument representing the lock is

/// available".

/// Mutex locks are the dynamic counterpart of control dependencies.

/// TODO(mdan): Properly document this thing.

///

/// Parametrization: TFT_MUTEX_LOCK[].

/// </summary>

[pbr::OriginalName("TFT_MUTEX_LOCK")] TftMutexLock = 10202,

/// <summary>

/// The equivalent of a Tensor with DT_VARIANT dtype, kept here to simplify

/// translation. This type should not normally appear after type inference.

/// Note that LEGACY_VARIANT != ANY: TENSOR[INT32] is a subtype of ANY, but is

/// not a subtype of LEGACY_VARIANT.

/// </summary>

[pbr::OriginalName("TFT_LEGACY_VARIANT")] TftLegacyVariant = 10203,

}

#endregion

#region Messages

/// <summary>

/// Highly experimental and very likely to change.

/// This encoding uses tags instead of dedicated messages for regularity. In

/// particular the encoding imposes no restrictions on what the parameters of any

/// type should be, which in particular needs to be true for type symbols.

/// </summary>

public sealed partial class FullTypeDef : pb::IMessage<FullTypeDef>

#if !GOOGLE_PROTOBUF_REFSTRUCT_COMPATIBILITY_MODE

, pb::IBufferMessage

#endif

{

private static readonly pb::MessageParser<FullTypeDef> _parser = new pb::MessageParser<FullTypeDef>(() => new FullTypeDef());

private pb::UnknownFieldSet _unknownFields;

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public static pb::MessageParser<FullTypeDef> Parser { get { return _parser; } }

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public static pbr::MessageDescriptor Descriptor {

get { return global::Tensorflow.FullTypeReflection.Descriptor.MessageTypes[0]; }

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

pbr::MessageDescriptor pb::IMessage.Descriptor {

get { return Descriptor; }

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public FullTypeDef() {

OnConstruction();

}

partial void OnConstruction();

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public FullTypeDef(FullTypeDef other) : this() {

typeId_ = other.typeId_;

args_ = other.args_.Clone();

switch (other.AttrCase) {

case AttrOneofCase.S:

S = other.S;

break;

case AttrOneofCase.I:

I = other.I;

break;

}

_unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields);

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public FullTypeDef Clone() {

return new FullTypeDef(this);

}

/// <summary>Field number for the "type_id" field.</summary>

public const int TypeIdFieldNumber = 1;

private global::Tensorflow.FullTypeId typeId_ = global::Tensorflow.FullTypeId.TftUnset;

/// <summary>

/// The principal type represented by this object. This may be a concrete type

/// (Tensor, Dataset) a type variable (used for dependent types) a type

/// symbol (Any, Union). See FullTypeId for details.

/// </summary>

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public global::Tensorflow.FullTypeId TypeId {

get { return typeId_; }

set {

typeId_ = value;

}

/// <summary>Field number for the "args" field.</summary>

public const int ArgsFieldNumber = 2;

private static readonly pb::FieldCodec<global::Tensorflow.FullTypeDef> _repeated_args_codec

= pb::FieldCodec.ForMessage(18, global::Tensorflow.FullTypeDef.Parser);

private readonly pbc::RepeatedField<global::Tensorflow.FullTypeDef> args_ = new pbc::RepeatedField<global::Tensorflow.FullTypeDef>();

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public pbc::RepeatedField<global::Tensorflow.FullTypeDef> Args {

get { return args_; }

}

/// <summary>Field number for the "s" field.</summary>

public const int SFieldNumber = 3;

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public string S {

get { return attrCase_ == AttrOneofCase.S ? (string) attr_ : ""; }

set {

attr_ = pb::ProtoPreconditions.CheckNotNull(value, "value");

attrCase_ = AttrOneofCase.S;

}

/// <summary>Field number for the "i" field.</summary>

public const int IFieldNumber = 4;

/// <summary>

/// TODO(mdan): list/tensor, map? Need to reconcile with TFT_RECORD, etc.

/// </summary>

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public long I {

get { return attrCase_ == AttrOneofCase.I ? (long) attr_ : 0L; }

set {

attr_ = value;

attrCase_ = AttrOneofCase.I;

}

private object attr_;

/// <summary>Enum of possible cases for the "attr" oneof.</summary>

public enum AttrOneofCase {

None = 0,

S = 3,

I = 4,

}

private AttrOneofCase attrCase_ = AttrOneofCase.None;

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public AttrOneofCase AttrCase {

get { return attrCase_; }

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public void ClearAttr() {

attrCase_ = AttrOneofCase.None;

attr_ = null;

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public override bool Equals(object other) {

return Equals(other as FullTypeDef);

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public bool Equals(FullTypeDef other) {

if (ReferenceEquals(other, null)) {

return false;

}

if (ReferenceEquals(other, this)) {

return true;

}

if (TypeId != other.TypeId) return false;

if(!args_.Equals(other.args_)) return false;

if (S != other.S) return false;

if (I != other.I) return false;

if (AttrCase != other.AttrCase) return false;

return Equals(_unknownFields, other._unknownFields);

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public override int GetHashCode() {

int hash = 1;

if (TypeId != global::Tensorflow.FullTypeId.TftUnset) hash ^= TypeId.GetHashCode();

hash ^= args_.GetHashCode();

if (attrCase_ == AttrOneofCase.S) hash ^= S.GetHashCode();

if (attrCase_ == AttrOneofCase.I) hash ^= I.GetHashCode();

hash ^= (int) attrCase_;

if (_unknownFields != null) {

hash ^= _unknownFields.GetHashCode();

}

return hash;

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public override string ToString() {

return pb::JsonFormatter.ToDiagnosticString(this);

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public void WriteTo(pb::CodedOutputStream output) {

#if !GOOGLE_PROTOBUF_REFSTRUCT_COMPATIBILITY_MODE

output.WriteRawMessage(this);

#else

if (TypeId != global::Tensorflow.FullTypeId.TftUnset) {

output.WriteRawTag(8);

output.WriteEnum((int) TypeId);

}

args_.WriteTo(output, _repeated_args_codec);

if (attrCase_ == AttrOneofCase.S) {

output.WriteRawTag(26);

output.WriteString(S);

}

if (attrCase_ == AttrOneofCase.I) {

output.WriteRawTag(32);

output.WriteInt64(I);

}

if (_unknownFields != null) {

_unknownFields.WriteTo(output);

}

#endif

}

#if !GOOGLE_PROTOBUF_REFSTRUCT_COMPATIBILITY_MODE

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

void pb::IBufferMessage.InternalWriteTo(ref pb::WriteContext output) {

if (TypeId != global::Tensorflow.FullTypeId.TftUnset) {

output.WriteRawTag(8);

output.WriteEnum((int) TypeId);

}

args_.WriteTo(ref output, _repeated_args_codec);

if (attrCase_ == AttrOneofCase.S) {

output.WriteRawTag(26);

output.WriteString(S);

}

if (attrCase_ == AttrOneofCase.I) {

output.WriteRawTag(32);

output.WriteInt64(I);

}

if (_unknownFields != null) {

_unknownFields.WriteTo(ref output);

}

#endif

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public int CalculateSize() {

int size = 0;

if (TypeId != global::Tensorflow.FullTypeId.TftUnset) {

size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) TypeId);

}

size += args_.CalculateSize(_repeated_args_codec);

if (attrCase_ == AttrOneofCase.S) {

size += 1 + pb::CodedOutputStream.ComputeStringSize(S);

}

if (attrCase_ == AttrOneofCase.I) {

size += 1 + pb::CodedOutputStream.ComputeInt64Size(I);

}

if (_unknownFields != null) {

size += _unknownFields.CalculateSize();

}

return size;

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public void MergeFrom(FullTypeDef other) {

if (other == null) {

return;

}

if (other.TypeId != global::Tensorflow.FullTypeId.TftUnset) {

TypeId = other.TypeId;

}

args_.Add(other.args_);

switch (other.AttrCase) {

case AttrOneofCase.S:

S = other.S;

break;

case AttrOneofCase.I:

I = other.I;

break;

}

_unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields);

}

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

public void MergeFrom(pb::CodedInputStream input) {

#if !GOOGLE_PROTOBUF_REFSTRUCT_COMPATIBILITY_MODE

input.ReadRawMessage(this);

#else

uint tag;

while ((tag = input.ReadTag()) != 0) {

switch(tag) {

default:

_unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input);

break;

case 8: {

TypeId = (global::Tensorflow.FullTypeId) input.ReadEnum();

break;

}

case 18: {

args_.AddEntriesFrom(input, _repeated_args_codec);

break;

}

case 26: {

S = input.ReadString();

break;

}

case 32: {

I = input.ReadInt64();

break;

}

#endif

}

#if !GOOGLE_PROTOBUF_REFSTRUCT_COMPATIBILITY_MODE

[global::System.Diagnostics.DebuggerNonUserCodeAttribute]

[global::System.CodeDom.Compiler.GeneratedCode("protoc", null)]

void pb::IBufferMessage.InternalMergeFrom(ref pb::ParseContext input) {

uint tag;

while ((tag = input.ReadTag()) != 0) {

switch(tag) {

default:

_unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, ref input);

break;

case 8: {

TypeId = (global::Tensorflow.FullTypeId) input.ReadEnum();

break;

}

case 18: {

args_.AddEntriesFrom(ref input, _repeated_args_codec);

break;

}

case 26: {

S = input.ReadString();

break;

}

case 32: {

I = input.ReadInt64();

break;

}

#endif

}

#endregion

}

#endregion Designer generated code

TensorFlow.NET/src/TensorFlowNET.Core/Protobuf/FullType.cs at master · hartoto2/TensorFlow.NET · GitHub

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