/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.*/
// Modification Copyright (C) 2018-2026 Intel Corporation

syntax = "proto3";

package tensorflow;

import "ov_tensorflow/resource_handle.proto";
import "ov_tensorflow/tensor_shape.proto";
import "ov_tensorflow/types.proto";

option cc_enable_arenas = true;
option java_outer_classname = "TensorProtos";
option java_multiple_files = true;
option java_package = "org.tensorflow.framework";
option go_package = "github.com/tensorflow/tensorflow/tensorflow/go/core/framework/tensor_go_proto";

// Protocol buffer representing a tensor.
message TensorProto {
  DataType dtype = 1;

  // Shape of the tensor.  TODO(touts): sort out the 0-rank issues.
  TensorShapeProto tensor_shape = 2;

  // 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.
  int32 version_number = 3;

  // 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.
  bytes tensor_content = 4;

  // 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.
  repeated int32 half_val = 13 [packed = true];

  // DT_FLOAT.
  repeated float float_val = 5 [packed = true];

  // DT_DOUBLE.
  repeated double double_val = 6 [packed = true];

  // DT_INT32, DT_INT16, DT_INT8, DT_UINT8.
  repeated int32 int_val = 7 [packed = true];

  // DT_STRING
  repeated bytes string_val = 8;

  // DT_COMPLEX64. scomplex_val(2*i) and scomplex_val(2*i+1) are real
  // and imaginary parts of i-th single precision complex.
  repeated float scomplex_val = 9 [packed = true];

  // DT_INT64
  repeated int64 int64_val = 10 [packed = true];

  // DT_BOOL
  repeated bool bool_val = 11 [packed = true];

  // DT_COMPLEX128. dcomplex_val(2*i) and dcomplex_val(2*i+1) are real
  // and imaginary parts of i-th double precision complex.
  repeated double dcomplex_val = 12 [packed = true];

  // DT_RESOURCE
  repeated ResourceHandleProto resource_handle_val = 14;

  // DT_VARIANT
  repeated VariantTensorDataProto variant_val = 15;

  // DT_UINT32
  repeated uint32 uint32_val = 16 [packed = true];

  // DT_UINT64
  repeated uint64 uint64_val = 17 [packed = true];
}

// Protocol buffer representing the serialization format of DT_VARIANT tensors.
message VariantTensorDataProto {
  // Name of the type of objects being serialized.
  string type_name = 1;
  // Portions of the object that are not Tensors.
  bytes metadata = 2;
  // Tensors contained within objects being serialized.
  repeated TensorProto tensors = 3;
}
