repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
java.util.Listrepeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
int getValuesCount();
/**
* repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
float getValues(int index);
// repeated uint64 keys = 2 [packed = true];
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
int getKeysCount();
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
long getKeys(int index);
// repeated uint64 shape = 3 [packed = true];
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
int getShapeCount();
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
long getShape(int index);
}
/**
* Protobuf type {@code aialgorithms.proto2.Float32Tensor}
*
* * A sparse or dense rank-R tensor that stores data as doubles (float64). **/ public static final class Float32Tensor extends com.google.protobuf.GeneratedMessage implements Float32TensorOrBuilder { // Use Float32Tensor.newBuilder() to construct. private Float32Tensor(com.google.protobuf.GeneratedMessage.Builder builder) { super(builder); this.unknownFields = builder.getUnknownFields(); } private Float32Tensor(boolean noInit) { this.unknownFields = com.google.protobuf.UnknownFieldSet.getDefaultInstance(); } private static final Float32Tensor defaultInstance; public static Float32Tensor getDefaultInstance() { return defaultInstance; } public Float32Tensor getDefaultInstanceForType() { return defaultInstance; } private final com.google.protobuf.UnknownFieldSet unknownFields; @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } private Float32Tensor( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { initFields(); int mutable_bitField0_ = 0; com.google.protobuf.UnknownFieldSet.Builder unknownFields = com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; default: { if (!parseUnknownField(input, unknownFields, extensionRegistry, tag)) { done = true; } break; } case 13: { if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { values_ = new java.util.ArrayList
repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public java.util.Listrepeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public int getValuesCount() {
return values_.size();
}
/**
* repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public float getValues(int index) {
return values_.get(index);
}
private int valuesMemoizedSerializedSize = -1;
// repeated uint64 keys = 2 [packed = true];
public static final int KEYS_FIELD_NUMBER = 2;
private java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public int getKeysCount() {
return keys_.size();
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public long getKeys(int index) {
return keys_.get(index);
}
private int keysMemoizedSerializedSize = -1;
// repeated uint64 shape = 3 [packed = true];
public static final int SHAPE_FIELD_NUMBER = 3;
private java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public int getShapeCount() {
return shape_.size();
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public long getShape(int index) {
return shape_.get(index);
}
private int shapeMemoizedSerializedSize = -1;
private void initFields() {
values_ = java.util.Collections.emptyList();
keys_ = java.util.Collections.emptyList();
shape_ = java.util.Collections.emptyList();
}
private byte memoizedIsInitialized = -1;
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized != -1) return isInitialized == 1;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException {
getSerializedSize();
if (getValuesList().size() > 0) {
output.writeRawVarint32(10);
output.writeRawVarint32(valuesMemoizedSerializedSize);
}
for (int i = 0; i < values_.size(); i++) {
output.writeFloatNoTag(values_.get(i));
}
if (getKeysList().size() > 0) {
output.writeRawVarint32(18);
output.writeRawVarint32(keysMemoizedSerializedSize);
}
for (int i = 0; i < keys_.size(); i++) {
output.writeUInt64NoTag(keys_.get(i));
}
if (getShapeList().size() > 0) {
output.writeRawVarint32(26);
output.writeRawVarint32(shapeMemoizedSerializedSize);
}
for (int i = 0; i < shape_.size(); i++) {
output.writeUInt64NoTag(shape_.get(i));
}
getUnknownFields().writeTo(output);
}
private int memoizedSerializedSize = -1;
public int getSerializedSize() {
int size = memoizedSerializedSize;
if (size != -1) return size;
size = 0;
{
int dataSize = 0;
dataSize = 4 * getValuesList().size();
size += dataSize;
if (!getValuesList().isEmpty()) {
size += 1;
size += com.google.protobuf.CodedOutputStream
.computeInt32SizeNoTag(dataSize);
}
valuesMemoizedSerializedSize = dataSize;
}
{
int dataSize = 0;
for (int i = 0; i < keys_.size(); i++) {
dataSize += com.google.protobuf.CodedOutputStream
.computeUInt64SizeNoTag(keys_.get(i));
}
size += dataSize;
if (!getKeysList().isEmpty()) {
size += 1;
size += com.google.protobuf.CodedOutputStream
.computeInt32SizeNoTag(dataSize);
}
keysMemoizedSerializedSize = dataSize;
}
{
int dataSize = 0;
for (int i = 0; i < shape_.size(); i++) {
dataSize += com.google.protobuf.CodedOutputStream
.computeUInt64SizeNoTag(shape_.get(i));
}
size += dataSize;
if (!getShapeList().isEmpty()) {
size += 1;
size += com.google.protobuf.CodedOutputStream
.computeInt32SizeNoTag(dataSize);
}
shapeMemoizedSerializedSize = dataSize;
}
size += getUnknownFields().getSerializedSize();
memoizedSerializedSize = size;
return size;
}
private static final long serialVersionUID = 0L;
@java.lang.Override
protected java.lang.Object writeReplace()
throws java.io.ObjectStreamException {
return super.writeReplace();
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Float32Tensor parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static Builder newBuilder() { return Builder.create(); }
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder(aialgorithms.proto2.RecordProto2.Float32Tensor prototype) {
return newBuilder().mergeFrom(prototype);
}
public Builder toBuilder() { return newBuilder(this); }
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessage.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
* Protobuf type {@code aialgorithms.proto2.Float32Tensor}
*
*
* A sparse or dense rank-R tensor that stores data as doubles (float64).
*
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessage.Builderrepeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public java.util.Listrepeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public int getValuesCount() {
return values_.size();
}
/**
* repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public float getValues(int index) {
return values_.get(index);
}
/**
* repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder setValues(
int index, float value) {
ensureValuesIsMutable();
values_.set(index, value);
onChanged();
return this;
}
/**
* repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder addValues(float value) {
ensureValuesIsMutable();
values_.add(value);
onChanged();
return this;
}
/**
* repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder addAllValues(
java.lang.Iterable values) {
ensureValuesIsMutable();
super.addAll(values, values_);
onChanged();
return this;
}
/**
* repeated float values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder clearValues() {
values_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
// repeated uint64 keys = 2 [packed = true];
private java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public int getKeysCount() {
return keys_.size();
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public long getKeys(int index) {
return keys_.get(index);
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder setKeys(
int index, long value) {
ensureKeysIsMutable();
keys_.set(index, value);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder addKeys(long value) {
ensureKeysIsMutable();
keys_.add(value);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder addAllKeys(
java.lang.Iterable values) {
ensureKeysIsMutable();
super.addAll(values, keys_);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder clearKeys() {
keys_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
return this;
}
// repeated uint64 shape = 3 [packed = true];
private java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public int getShapeCount() {
return shape_.size();
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public long getShape(int index) {
return shape_.get(index);
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder setShape(
int index, long value) {
ensureShapeIsMutable();
shape_.set(index, value);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder addShape(long value) {
ensureShapeIsMutable();
shape_.add(value);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder addAllShape(
java.lang.Iterable values) {
ensureShapeIsMutable();
super.addAll(values, shape_);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder clearShape() {
shape_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
// @@protoc_insertion_point(builder_scope:aialgorithms.proto2.Float32Tensor)
}
static {
defaultInstance = new Float32Tensor(true);
defaultInstance.initFields();
}
// @@protoc_insertion_point(class_scope:aialgorithms.proto2.Float32Tensor)
}
public interface Float64TensorOrBuilder
extends com.google.protobuf.MessageOrBuilder {
// repeated double values = 1 [packed = true];
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
java.util.Listrepeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
int getValuesCount();
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
double getValues(int index);
// repeated uint64 keys = 2 [packed = true];
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
int getKeysCount();
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
long getKeys(int index);
// repeated uint64 shape = 3 [packed = true];
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
int getShapeCount();
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
long getShape(int index);
}
/**
* Protobuf type {@code aialgorithms.proto2.Float64Tensor}
*
* * A sparse or dense rank-R tensor that stores data as doubles (float64). **/ public static final class Float64Tensor extends com.google.protobuf.GeneratedMessage implements Float64TensorOrBuilder { // Use Float64Tensor.newBuilder() to construct. private Float64Tensor(com.google.protobuf.GeneratedMessage.Builder builder) { super(builder); this.unknownFields = builder.getUnknownFields(); } private Float64Tensor(boolean noInit) { this.unknownFields = com.google.protobuf.UnknownFieldSet.getDefaultInstance(); } private static final Float64Tensor defaultInstance; public static Float64Tensor getDefaultInstance() { return defaultInstance; } public Float64Tensor getDefaultInstanceForType() { return defaultInstance; } private final com.google.protobuf.UnknownFieldSet unknownFields; @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } private Float64Tensor( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { initFields(); int mutable_bitField0_ = 0; com.google.protobuf.UnknownFieldSet.Builder unknownFields = com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; default: { if (!parseUnknownField(input, unknownFields, extensionRegistry, tag)) { done = true; } break; } case 9: { if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { values_ = new java.util.ArrayList
repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public java.util.Listrepeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public int getValuesCount() {
return values_.size();
}
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public double getValues(int index) {
return values_.get(index);
}
private int valuesMemoizedSerializedSize = -1;
// repeated uint64 keys = 2 [packed = true];
public static final int KEYS_FIELD_NUMBER = 2;
private java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public int getKeysCount() {
return keys_.size();
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public long getKeys(int index) {
return keys_.get(index);
}
private int keysMemoizedSerializedSize = -1;
// repeated uint64 shape = 3 [packed = true];
public static final int SHAPE_FIELD_NUMBER = 3;
private java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public int getShapeCount() {
return shape_.size();
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public long getShape(int index) {
return shape_.get(index);
}
private int shapeMemoizedSerializedSize = -1;
private void initFields() {
values_ = java.util.Collections.emptyList();
keys_ = java.util.Collections.emptyList();
shape_ = java.util.Collections.emptyList();
}
private byte memoizedIsInitialized = -1;
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized != -1) return isInitialized == 1;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException {
getSerializedSize();
if (getValuesList().size() > 0) {
output.writeRawVarint32(10);
output.writeRawVarint32(valuesMemoizedSerializedSize);
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output.writeRawVarint32(26);
output.writeRawVarint32(shapeMemoizedSerializedSize);
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for (int i = 0; i < shape_.size(); i++) {
output.writeUInt64NoTag(shape_.get(i));
}
getUnknownFields().writeTo(output);
}
private int memoizedSerializedSize = -1;
public int getSerializedSize() {
int size = memoizedSerializedSize;
if (size != -1) return size;
size = 0;
{
int dataSize = 0;
dataSize = 8 * getValuesList().size();
size += dataSize;
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size += com.google.protobuf.CodedOutputStream
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valuesMemoizedSerializedSize = dataSize;
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{
int dataSize = 0;
for (int i = 0; i < keys_.size(); i++) {
dataSize += com.google.protobuf.CodedOutputStream
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size += com.google.protobuf.CodedOutputStream
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for (int i = 0; i < shape_.size(); i++) {
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size += getUnknownFields().getSerializedSize();
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private static final long serialVersionUID = 0L;
@java.lang.Override
protected java.lang.Object writeReplace()
throws java.io.ObjectStreamException {
return super.writeReplace();
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Float64Tensor parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static Builder newBuilder() { return Builder.create(); }
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder(aialgorithms.proto2.RecordProto2.Float64Tensor prototype) {
return newBuilder().mergeFrom(prototype);
}
public Builder toBuilder() { return newBuilder(this); }
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessage.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
* Protobuf type {@code aialgorithms.proto2.Float64Tensor}
*
*
* A sparse or dense rank-R tensor that stores data as doubles (float64).
*
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessage.Builderrepeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public java.util.Listrepeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public int getValuesCount() {
return values_.size();
}
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public double getValues(int index) {
return values_.get(index);
}
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder setValues(
int index, double value) {
ensureValuesIsMutable();
values_.set(index, value);
onChanged();
return this;
}
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder addValues(double value) {
ensureValuesIsMutable();
values_.add(value);
onChanged();
return this;
}
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder addAllValues(
java.lang.Iterable values) {
ensureValuesIsMutable();
super.addAll(values, values_);
onChanged();
return this;
}
/**
* repeated double values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder clearValues() {
values_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
// repeated uint64 keys = 2 [packed = true];
private java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public int getKeysCount() {
return keys_.size();
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public long getKeys(int index) {
return keys_.get(index);
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder setKeys(
int index, long value) {
ensureKeysIsMutable();
keys_.set(index, value);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder addKeys(long value) {
ensureKeysIsMutable();
keys_.add(value);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder addAllKeys(
java.lang.Iterable values) {
ensureKeysIsMutable();
super.addAll(values, keys_);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder clearKeys() {
keys_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
return this;
}
// repeated uint64 shape = 3 [packed = true];
private java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public int getShapeCount() {
return shape_.size();
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public long getShape(int index) {
return shape_.get(index);
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder setShape(
int index, long value) {
ensureShapeIsMutable();
shape_.set(index, value);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder addShape(long value) {
ensureShapeIsMutable();
shape_.add(value);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder addAllShape(
java.lang.Iterable values) {
ensureShapeIsMutable();
super.addAll(values, shape_);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder clearShape() {
shape_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
// @@protoc_insertion_point(builder_scope:aialgorithms.proto2.Float64Tensor)
}
static {
defaultInstance = new Float64Tensor(true);
defaultInstance.initFields();
}
// @@protoc_insertion_point(class_scope:aialgorithms.proto2.Float64Tensor)
}
public interface Int32TensorOrBuilder
extends com.google.protobuf.MessageOrBuilder {
// repeated int32 values = 1 [packed = true];
/**
* repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
java.util.Listrepeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
int getValuesCount();
/**
* repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
int getValues(int index);
// repeated uint64 keys = 2 [packed = true];
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
int getKeysCount();
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
long getKeys(int index);
// repeated uint64 shape = 3 [packed = true];
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
int getShapeCount();
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
long getShape(int index);
}
/**
* Protobuf type {@code aialgorithms.proto2.Int32Tensor}
*
* * A sparse or dense rank-R tensor that stores data as 32-bit ints (int32). **/ public static final class Int32Tensor extends com.google.protobuf.GeneratedMessage implements Int32TensorOrBuilder { // Use Int32Tensor.newBuilder() to construct. private Int32Tensor(com.google.protobuf.GeneratedMessage.Builder builder) { super(builder); this.unknownFields = builder.getUnknownFields(); } private Int32Tensor(boolean noInit) { this.unknownFields = com.google.protobuf.UnknownFieldSet.getDefaultInstance(); } private static final Int32Tensor defaultInstance; public static Int32Tensor getDefaultInstance() { return defaultInstance; } public Int32Tensor getDefaultInstanceForType() { return defaultInstance; } private final com.google.protobuf.UnknownFieldSet unknownFields; @java.lang.Override public final com.google.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } private Int32Tensor( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { initFields(); int mutable_bitField0_ = 0; com.google.protobuf.UnknownFieldSet.Builder unknownFields = com.google.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; default: { if (!parseUnknownField(input, unknownFields, extensionRegistry, tag)) { done = true; } break; } case 8: { if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { values_ = new java.util.ArrayList
repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public java.util.Listrepeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public int getValuesCount() {
return values_.size();
}
/**
* repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public int getValues(int index) {
return values_.get(index);
}
private int valuesMemoizedSerializedSize = -1;
// repeated uint64 keys = 2 [packed = true];
public static final int KEYS_FIELD_NUMBER = 2;
private java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public int getKeysCount() {
return keys_.size();
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public long getKeys(int index) {
return keys_.get(index);
}
private int keysMemoizedSerializedSize = -1;
// repeated uint64 shape = 3 [packed = true];
public static final int SHAPE_FIELD_NUMBER = 3;
private java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public int getShapeCount() {
return shape_.size();
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public long getShape(int index) {
return shape_.get(index);
}
private int shapeMemoizedSerializedSize = -1;
private void initFields() {
values_ = java.util.Collections.emptyList();
keys_ = java.util.Collections.emptyList();
shape_ = java.util.Collections.emptyList();
}
private byte memoizedIsInitialized = -1;
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized != -1) return isInitialized == 1;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException {
getSerializedSize();
if (getValuesList().size() > 0) {
output.writeRawVarint32(10);
output.writeRawVarint32(valuesMemoizedSerializedSize);
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for (int i = 0; i < values_.size(); i++) {
output.writeInt32NoTag(values_.get(i));
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if (getKeysList().size() > 0) {
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output.writeUInt64NoTag(shape_.get(i));
}
getUnknownFields().writeTo(output);
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private int memoizedSerializedSize = -1;
public int getSerializedSize() {
int size = memoizedSerializedSize;
if (size != -1) return size;
size = 0;
{
int dataSize = 0;
for (int i = 0; i < values_.size(); i++) {
dataSize += com.google.protobuf.CodedOutputStream
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size += dataSize;
if (!getValuesList().isEmpty()) {
size += 1;
size += com.google.protobuf.CodedOutputStream
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}
valuesMemoizedSerializedSize = dataSize;
}
{
int dataSize = 0;
for (int i = 0; i < keys_.size(); i++) {
dataSize += com.google.protobuf.CodedOutputStream
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}
size += dataSize;
if (!getKeysList().isEmpty()) {
size += 1;
size += com.google.protobuf.CodedOutputStream
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keysMemoizedSerializedSize = dataSize;
}
{
int dataSize = 0;
for (int i = 0; i < shape_.size(); i++) {
dataSize += com.google.protobuf.CodedOutputStream
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size += dataSize;
if (!getShapeList().isEmpty()) {
size += 1;
size += com.google.protobuf.CodedOutputStream
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}
shapeMemoizedSerializedSize = dataSize;
}
size += getUnknownFields().getSerializedSize();
memoizedSerializedSize = size;
return size;
}
private static final long serialVersionUID = 0L;
@java.lang.Override
protected java.lang.Object writeReplace()
throws java.io.ObjectStreamException {
return super.writeReplace();
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Int32Tensor parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static Builder newBuilder() { return Builder.create(); }
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder(aialgorithms.proto2.RecordProto2.Int32Tensor prototype) {
return newBuilder().mergeFrom(prototype);
}
public Builder toBuilder() { return newBuilder(this); }
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessage.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
* Protobuf type {@code aialgorithms.proto2.Int32Tensor}
*
*
* A sparse or dense rank-R tensor that stores data as 32-bit ints (int32).
*
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessage.Builderrepeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public java.util.Listrepeated int32 values = 1 [packed = true];
*
*
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return values_.size();
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/**
* repeated int32 values = 1 [packed = true];
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*
* Each value in the vector. If keys is empty this is treated as a
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/**
* repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
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public Builder setValues(
int index, int value) {
ensureValuesIsMutable();
values_.set(index, value);
onChanged();
return this;
}
/**
* repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder addValues(int value) {
ensureValuesIsMutable();
values_.add(value);
onChanged();
return this;
}
/**
* repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
* dense vector.
*
*/
public Builder addAllValues(
java.lang.Iterable values) {
ensureValuesIsMutable();
super.addAll(values, values_);
onChanged();
return this;
}
/**
* repeated int32 values = 1 [packed = true];
*
*
* Each value in the vector. If keys is empty this is treated as a
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*
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public Builder clearValues() {
values_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
return this;
}
// repeated uint64 keys = 2 [packed = true];
private java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public java.util.Listrepeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
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/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
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/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder setKeys(
int index, long value) {
ensureKeysIsMutable();
keys_.set(index, value);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder addKeys(long value) {
ensureKeysIsMutable();
keys_.add(value);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder addAllKeys(
java.lang.Iterable values) {
ensureKeysIsMutable();
super.addAll(values, keys_);
onChanged();
return this;
}
/**
* repeated uint64 keys = 2 [packed = true];
*
*
* If not empty then the vector is treated as sparse with
* each key specifying the location of the value in the sparse vector.
*
*/
public Builder clearKeys() {
keys_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
return this;
}
// repeated uint64 shape = 3 [packed = true];
private java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public java.util.Listrepeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public int getShapeCount() {
return shape_.size();
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public long getShape(int index) {
return shape_.get(index);
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder setShape(
int index, long value) {
ensureShapeIsMutable();
shape_.set(index, value);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder addShape(long value) {
ensureShapeIsMutable();
shape_.add(value);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder addAllShape(
java.lang.Iterable values) {
ensureShapeIsMutable();
super.addAll(values, shape_);
onChanged();
return this;
}
/**
* repeated uint64 shape = 3 [packed = true];
*
*
* Optional shape which will allow the vector to represent a matrix.
* e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
* and keys[i] % 20 will give the column.
* This also supports n-dimensonal tensors.
* NB. this must be specified if the tensor is sparse.
*
*/
public Builder clearShape() {
shape_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000004);
onChanged();
return this;
}
// @@protoc_insertion_point(builder_scope:aialgorithms.proto2.Int32Tensor)
}
static {
defaultInstance = new Int32Tensor(true);
defaultInstance.initFields();
}
// @@protoc_insertion_point(class_scope:aialgorithms.proto2.Int32Tensor)
}
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extends com.google.protobuf.MessageOrBuilder {
// repeated bytes value = 1;
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com.google.protobuf.ByteString getValue(int index);
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* optional string content_type = 2;
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*
* Stores the content type of the data if known.
* This will allow the possibility of using decoders for common formats
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*
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boolean hasContentType();
/**
* optional string content_type = 2;
*
*
* Stores the content type of the data if known.
* This will allow the possibility of using decoders for common formats
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*
*/
java.lang.String getContentType();
/**
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*
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* This will allow the possibility of using decoders for common formats
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com.google.protobuf.ByteString
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/**
* Protobuf type {@code aialgorithms.proto2.Bytes}
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* This will allow the possibility of using decoders for common formats
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*
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* This will allow the possibility of using decoders for common formats
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*
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* This will allow the possibility of using decoders for common formats
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java.lang.Object ref = contentType_;
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getSerializedSize();
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public static aialgorithms.proto2.RecordProto2.Bytes parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static aialgorithms.proto2.RecordProto2.Bytes parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static aialgorithms.proto2.RecordProto2.Bytes parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static aialgorithms.proto2.RecordProto2.Bytes parseFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Bytes parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
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public static aialgorithms.proto2.RecordProto2.Bytes parseDelimitedFrom(
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com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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return PARSER.parseDelimitedFrom(input, extensionRegistry);
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public static aialgorithms.proto2.RecordProto2.Bytes parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
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public static aialgorithms.proto2.RecordProto2.Bytes parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
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public static Builder newBuilder() { return Builder.create(); }
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder(aialgorithms.proto2.RecordProto2.Bytes prototype) {
return newBuilder().mergeFrom(prototype);
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public Builder toBuilder() { return newBuilder(this); }
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessage.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
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/**
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*
*
* Support for storing binary data for parsing in other ways (such as JPEG/etc).
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public static final class Builder extends
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onChanged();
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value_.add(value);
onChanged();
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onChanged();
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value_ = java.util.Collections.emptyList();
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onChanged();
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* This will allow the possibility of using decoders for common formats
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* This will allow the possibility of using decoders for common formats
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* This will allow the possibility of using decoders for common formats
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contentType_ = getDefaultInstance().getContentType();
onChanged();
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defaultInstance.initFields();
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// @@protoc_insertion_point(class_scope:aialgorithms.proto2.Bytes)
}
public interface ValueOrBuilder
extends com.google.protobuf.MessageOrBuilder {
// optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
boolean hasFloat32Tensor();
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
aialgorithms.proto2.RecordProto2.Float32Tensor getFloat32Tensor();
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
aialgorithms.proto2.RecordProto2.Float32TensorOrBuilder getFloat32TensorOrBuilder();
// optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
boolean hasFloat64Tensor();
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
aialgorithms.proto2.RecordProto2.Float64Tensor getFloat64Tensor();
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
aialgorithms.proto2.RecordProto2.Float64TensorOrBuilder getFloat64TensorOrBuilder();
// optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
boolean hasInt32Tensor();
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
aialgorithms.proto2.RecordProto2.Int32Tensor getInt32Tensor();
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
aialgorithms.proto2.RecordProto2.Int32TensorOrBuilder getInt32TensorOrBuilder();
// optional .aialgorithms.proto2.Bytes bytes = 9;
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
boolean hasBytes();
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
aialgorithms.proto2.RecordProto2.Bytes getBytes();
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
aialgorithms.proto2.RecordProto2.BytesOrBuilder getBytesOrBuilder();
}
/**
* Protobuf type {@code aialgorithms.proto2.Value}
*/
public static final class Value extends
com.google.protobuf.GeneratedMessage
implements ValueOrBuilder {
// Use Value.newBuilder() to construct.
private Value(com.google.protobuf.GeneratedMessage.Builder builder) {
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float32Tensor_ = input.readMessage(aialgorithms.proto2.RecordProto2.Float32Tensor.PARSER, extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(float32Tensor_);
float32Tensor_ = subBuilder.buildPartial();
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case 26: {
aialgorithms.proto2.RecordProto2.Float64Tensor.Builder subBuilder = null;
if (((bitField0_ & 0x00000002) == 0x00000002)) {
subBuilder = float64Tensor_.toBuilder();
}
float64Tensor_ = input.readMessage(aialgorithms.proto2.RecordProto2.Float64Tensor.PARSER, extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(float64Tensor_);
float64Tensor_ = subBuilder.buildPartial();
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case 58: {
aialgorithms.proto2.RecordProto2.Int32Tensor.Builder subBuilder = null;
if (((bitField0_ & 0x00000004) == 0x00000004)) {
subBuilder = int32Tensor_.toBuilder();
}
int32Tensor_ = input.readMessage(aialgorithms.proto2.RecordProto2.Int32Tensor.PARSER, extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(int32Tensor_);
int32Tensor_ = subBuilder.buildPartial();
}
bitField0_ |= 0x00000004;
break;
}
case 74: {
aialgorithms.proto2.RecordProto2.Bytes.Builder subBuilder = null;
if (((bitField0_ & 0x00000008) == 0x00000008)) {
subBuilder = bytes_.toBuilder();
}
bytes_ = input.readMessage(aialgorithms.proto2.RecordProto2.Bytes.PARSER, extensionRegistry);
if (subBuilder != null) {
subBuilder.mergeFrom(bytes_);
bytes_ = subBuilder.buildPartial();
}
bitField0_ |= 0x00000008;
break;
}
}
}
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
throw e.setUnfinishedMessage(this);
} catch (java.io.IOException e) {
throw new com.google.protobuf.InvalidProtocolBufferException(
e.getMessage()).setUnfinishedMessage(this);
} finally {
this.unknownFields = unknownFields.build();
makeExtensionsImmutable();
}
}
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return aialgorithms.proto2.RecordProto2.internal_static_aialgorithms_proto2_Value_descriptor;
}
protected com.google.protobuf.GeneratedMessage.FieldAccessorTable
internalGetFieldAccessorTable() {
return aialgorithms.proto2.RecordProto2.internal_static_aialgorithms_proto2_Value_fieldAccessorTable
.ensureFieldAccessorsInitialized(
aialgorithms.proto2.RecordProto2.Value.class, aialgorithms.proto2.RecordProto2.Value.Builder.class);
}
public static com.google.protobuf.Parseroptional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public boolean hasFloat32Tensor() {
return ((bitField0_ & 0x00000001) == 0x00000001);
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public aialgorithms.proto2.RecordProto2.Float32Tensor getFloat32Tensor() {
return float32Tensor_;
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public aialgorithms.proto2.RecordProto2.Float32TensorOrBuilder getFloat32TensorOrBuilder() {
return float32Tensor_;
}
// optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
public static final int FLOAT64_TENSOR_FIELD_NUMBER = 3;
private aialgorithms.proto2.RecordProto2.Float64Tensor float64Tensor_;
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public boolean hasFloat64Tensor() {
return ((bitField0_ & 0x00000002) == 0x00000002);
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public aialgorithms.proto2.RecordProto2.Float64Tensor getFloat64Tensor() {
return float64Tensor_;
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public aialgorithms.proto2.RecordProto2.Float64TensorOrBuilder getFloat64TensorOrBuilder() {
return float64Tensor_;
}
// optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
public static final int INT32_TENSOR_FIELD_NUMBER = 7;
private aialgorithms.proto2.RecordProto2.Int32Tensor int32Tensor_;
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public boolean hasInt32Tensor() {
return ((bitField0_ & 0x00000004) == 0x00000004);
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public aialgorithms.proto2.RecordProto2.Int32Tensor getInt32Tensor() {
return int32Tensor_;
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public aialgorithms.proto2.RecordProto2.Int32TensorOrBuilder getInt32TensorOrBuilder() {
return int32Tensor_;
}
// optional .aialgorithms.proto2.Bytes bytes = 9;
public static final int BYTES_FIELD_NUMBER = 9;
private aialgorithms.proto2.RecordProto2.Bytes bytes_;
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public boolean hasBytes() {
return ((bitField0_ & 0x00000008) == 0x00000008);
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public aialgorithms.proto2.RecordProto2.Bytes getBytes() {
return bytes_;
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public aialgorithms.proto2.RecordProto2.BytesOrBuilder getBytesOrBuilder() {
return bytes_;
}
private void initFields() {
float32Tensor_ = aialgorithms.proto2.RecordProto2.Float32Tensor.getDefaultInstance();
float64Tensor_ = aialgorithms.proto2.RecordProto2.Float64Tensor.getDefaultInstance();
int32Tensor_ = aialgorithms.proto2.RecordProto2.Int32Tensor.getDefaultInstance();
bytes_ = aialgorithms.proto2.RecordProto2.Bytes.getDefaultInstance();
}
private byte memoizedIsInitialized = -1;
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized != -1) return isInitialized == 1;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException {
getSerializedSize();
if (((bitField0_ & 0x00000001) == 0x00000001)) {
output.writeMessage(2, float32Tensor_);
}
if (((bitField0_ & 0x00000002) == 0x00000002)) {
output.writeMessage(3, float64Tensor_);
}
if (((bitField0_ & 0x00000004) == 0x00000004)) {
output.writeMessage(7, int32Tensor_);
}
if (((bitField0_ & 0x00000008) == 0x00000008)) {
output.writeMessage(9, bytes_);
}
getUnknownFields().writeTo(output);
}
private int memoizedSerializedSize = -1;
public int getSerializedSize() {
int size = memoizedSerializedSize;
if (size != -1) return size;
size = 0;
if (((bitField0_ & 0x00000001) == 0x00000001)) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(2, float32Tensor_);
}
if (((bitField0_ & 0x00000002) == 0x00000002)) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(3, float64Tensor_);
}
if (((bitField0_ & 0x00000004) == 0x00000004)) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(7, int32Tensor_);
}
if (((bitField0_ & 0x00000008) == 0x00000008)) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(9, bytes_);
}
size += getUnknownFields().getSerializedSize();
memoizedSerializedSize = size;
return size;
}
private static final long serialVersionUID = 0L;
@java.lang.Override
protected java.lang.Object writeReplace()
throws java.io.ObjectStreamException {
return super.writeReplace();
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Value parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Value parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Value parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static Builder newBuilder() { return Builder.create(); }
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder(aialgorithms.proto2.RecordProto2.Value prototype) {
return newBuilder().mergeFrom(prototype);
}
public Builder toBuilder() { return newBuilder(this); }
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessage.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
* Protobuf type {@code aialgorithms.proto2.Value}
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessage.Builderoptional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public boolean hasFloat32Tensor() {
return ((bitField0_ & 0x00000001) == 0x00000001);
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public aialgorithms.proto2.RecordProto2.Float32Tensor getFloat32Tensor() {
if (float32TensorBuilder_ == null) {
return float32Tensor_;
} else {
return float32TensorBuilder_.getMessage();
}
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public Builder setFloat32Tensor(aialgorithms.proto2.RecordProto2.Float32Tensor value) {
if (float32TensorBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
float32Tensor_ = value;
onChanged();
} else {
float32TensorBuilder_.setMessage(value);
}
bitField0_ |= 0x00000001;
return this;
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public Builder setFloat32Tensor(
aialgorithms.proto2.RecordProto2.Float32Tensor.Builder builderForValue) {
if (float32TensorBuilder_ == null) {
float32Tensor_ = builderForValue.build();
onChanged();
} else {
float32TensorBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000001;
return this;
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public Builder mergeFloat32Tensor(aialgorithms.proto2.RecordProto2.Float32Tensor value) {
if (float32TensorBuilder_ == null) {
if (((bitField0_ & 0x00000001) == 0x00000001) &&
float32Tensor_ != aialgorithms.proto2.RecordProto2.Float32Tensor.getDefaultInstance()) {
float32Tensor_ =
aialgorithms.proto2.RecordProto2.Float32Tensor.newBuilder(float32Tensor_).mergeFrom(value).buildPartial();
} else {
float32Tensor_ = value;
}
onChanged();
} else {
float32TensorBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000001;
return this;
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public Builder clearFloat32Tensor() {
if (float32TensorBuilder_ == null) {
float32Tensor_ = aialgorithms.proto2.RecordProto2.Float32Tensor.getDefaultInstance();
onChanged();
} else {
float32TensorBuilder_.clear();
}
bitField0_ = (bitField0_ & ~0x00000001);
return this;
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public aialgorithms.proto2.RecordProto2.Float32Tensor.Builder getFloat32TensorBuilder() {
bitField0_ |= 0x00000001;
onChanged();
return getFloat32TensorFieldBuilder().getBuilder();
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
public aialgorithms.proto2.RecordProto2.Float32TensorOrBuilder getFloat32TensorOrBuilder() {
if (float32TensorBuilder_ != null) {
return float32TensorBuilder_.getMessageOrBuilder();
} else {
return float32Tensor_;
}
}
/**
* optional .aialgorithms.proto2.Float32Tensor float32_tensor = 2;
*/
private com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Float32Tensor, aialgorithms.proto2.RecordProto2.Float32Tensor.Builder, aialgorithms.proto2.RecordProto2.Float32TensorOrBuilder>
getFloat32TensorFieldBuilder() {
if (float32TensorBuilder_ == null) {
float32TensorBuilder_ = new com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Float32Tensor, aialgorithms.proto2.RecordProto2.Float32Tensor.Builder, aialgorithms.proto2.RecordProto2.Float32TensorOrBuilder>(
float32Tensor_,
getParentForChildren(),
isClean());
float32Tensor_ = null;
}
return float32TensorBuilder_;
}
// optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
private aialgorithms.proto2.RecordProto2.Float64Tensor float64Tensor_ = aialgorithms.proto2.RecordProto2.Float64Tensor.getDefaultInstance();
private com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Float64Tensor, aialgorithms.proto2.RecordProto2.Float64Tensor.Builder, aialgorithms.proto2.RecordProto2.Float64TensorOrBuilder> float64TensorBuilder_;
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public boolean hasFloat64Tensor() {
return ((bitField0_ & 0x00000002) == 0x00000002);
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public aialgorithms.proto2.RecordProto2.Float64Tensor getFloat64Tensor() {
if (float64TensorBuilder_ == null) {
return float64Tensor_;
} else {
return float64TensorBuilder_.getMessage();
}
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public Builder setFloat64Tensor(aialgorithms.proto2.RecordProto2.Float64Tensor value) {
if (float64TensorBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
float64Tensor_ = value;
onChanged();
} else {
float64TensorBuilder_.setMessage(value);
}
bitField0_ |= 0x00000002;
return this;
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public Builder setFloat64Tensor(
aialgorithms.proto2.RecordProto2.Float64Tensor.Builder builderForValue) {
if (float64TensorBuilder_ == null) {
float64Tensor_ = builderForValue.build();
onChanged();
} else {
float64TensorBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000002;
return this;
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public Builder mergeFloat64Tensor(aialgorithms.proto2.RecordProto2.Float64Tensor value) {
if (float64TensorBuilder_ == null) {
if (((bitField0_ & 0x00000002) == 0x00000002) &&
float64Tensor_ != aialgorithms.proto2.RecordProto2.Float64Tensor.getDefaultInstance()) {
float64Tensor_ =
aialgorithms.proto2.RecordProto2.Float64Tensor.newBuilder(float64Tensor_).mergeFrom(value).buildPartial();
} else {
float64Tensor_ = value;
}
onChanged();
} else {
float64TensorBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000002;
return this;
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public Builder clearFloat64Tensor() {
if (float64TensorBuilder_ == null) {
float64Tensor_ = aialgorithms.proto2.RecordProto2.Float64Tensor.getDefaultInstance();
onChanged();
} else {
float64TensorBuilder_.clear();
}
bitField0_ = (bitField0_ & ~0x00000002);
return this;
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public aialgorithms.proto2.RecordProto2.Float64Tensor.Builder getFloat64TensorBuilder() {
bitField0_ |= 0x00000002;
onChanged();
return getFloat64TensorFieldBuilder().getBuilder();
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
public aialgorithms.proto2.RecordProto2.Float64TensorOrBuilder getFloat64TensorOrBuilder() {
if (float64TensorBuilder_ != null) {
return float64TensorBuilder_.getMessageOrBuilder();
} else {
return float64Tensor_;
}
}
/**
* optional .aialgorithms.proto2.Float64Tensor float64_tensor = 3;
*/
private com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Float64Tensor, aialgorithms.proto2.RecordProto2.Float64Tensor.Builder, aialgorithms.proto2.RecordProto2.Float64TensorOrBuilder>
getFloat64TensorFieldBuilder() {
if (float64TensorBuilder_ == null) {
float64TensorBuilder_ = new com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Float64Tensor, aialgorithms.proto2.RecordProto2.Float64Tensor.Builder, aialgorithms.proto2.RecordProto2.Float64TensorOrBuilder>(
float64Tensor_,
getParentForChildren(),
isClean());
float64Tensor_ = null;
}
return float64TensorBuilder_;
}
// optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
private aialgorithms.proto2.RecordProto2.Int32Tensor int32Tensor_ = aialgorithms.proto2.RecordProto2.Int32Tensor.getDefaultInstance();
private com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Int32Tensor, aialgorithms.proto2.RecordProto2.Int32Tensor.Builder, aialgorithms.proto2.RecordProto2.Int32TensorOrBuilder> int32TensorBuilder_;
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public boolean hasInt32Tensor() {
return ((bitField0_ & 0x00000004) == 0x00000004);
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public aialgorithms.proto2.RecordProto2.Int32Tensor getInt32Tensor() {
if (int32TensorBuilder_ == null) {
return int32Tensor_;
} else {
return int32TensorBuilder_.getMessage();
}
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public Builder setInt32Tensor(aialgorithms.proto2.RecordProto2.Int32Tensor value) {
if (int32TensorBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
int32Tensor_ = value;
onChanged();
} else {
int32TensorBuilder_.setMessage(value);
}
bitField0_ |= 0x00000004;
return this;
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public Builder setInt32Tensor(
aialgorithms.proto2.RecordProto2.Int32Tensor.Builder builderForValue) {
if (int32TensorBuilder_ == null) {
int32Tensor_ = builderForValue.build();
onChanged();
} else {
int32TensorBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000004;
return this;
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public Builder mergeInt32Tensor(aialgorithms.proto2.RecordProto2.Int32Tensor value) {
if (int32TensorBuilder_ == null) {
if (((bitField0_ & 0x00000004) == 0x00000004) &&
int32Tensor_ != aialgorithms.proto2.RecordProto2.Int32Tensor.getDefaultInstance()) {
int32Tensor_ =
aialgorithms.proto2.RecordProto2.Int32Tensor.newBuilder(int32Tensor_).mergeFrom(value).buildPartial();
} else {
int32Tensor_ = value;
}
onChanged();
} else {
int32TensorBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000004;
return this;
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public Builder clearInt32Tensor() {
if (int32TensorBuilder_ == null) {
int32Tensor_ = aialgorithms.proto2.RecordProto2.Int32Tensor.getDefaultInstance();
onChanged();
} else {
int32TensorBuilder_.clear();
}
bitField0_ = (bitField0_ & ~0x00000004);
return this;
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public aialgorithms.proto2.RecordProto2.Int32Tensor.Builder getInt32TensorBuilder() {
bitField0_ |= 0x00000004;
onChanged();
return getInt32TensorFieldBuilder().getBuilder();
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
public aialgorithms.proto2.RecordProto2.Int32TensorOrBuilder getInt32TensorOrBuilder() {
if (int32TensorBuilder_ != null) {
return int32TensorBuilder_.getMessageOrBuilder();
} else {
return int32Tensor_;
}
}
/**
* optional .aialgorithms.proto2.Int32Tensor int32_tensor = 7;
*/
private com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Int32Tensor, aialgorithms.proto2.RecordProto2.Int32Tensor.Builder, aialgorithms.proto2.RecordProto2.Int32TensorOrBuilder>
getInt32TensorFieldBuilder() {
if (int32TensorBuilder_ == null) {
int32TensorBuilder_ = new com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Int32Tensor, aialgorithms.proto2.RecordProto2.Int32Tensor.Builder, aialgorithms.proto2.RecordProto2.Int32TensorOrBuilder>(
int32Tensor_,
getParentForChildren(),
isClean());
int32Tensor_ = null;
}
return int32TensorBuilder_;
}
// optional .aialgorithms.proto2.Bytes bytes = 9;
private aialgorithms.proto2.RecordProto2.Bytes bytes_ = aialgorithms.proto2.RecordProto2.Bytes.getDefaultInstance();
private com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Bytes, aialgorithms.proto2.RecordProto2.Bytes.Builder, aialgorithms.proto2.RecordProto2.BytesOrBuilder> bytesBuilder_;
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public boolean hasBytes() {
return ((bitField0_ & 0x00000008) == 0x00000008);
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public aialgorithms.proto2.RecordProto2.Bytes getBytes() {
if (bytesBuilder_ == null) {
return bytes_;
} else {
return bytesBuilder_.getMessage();
}
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public Builder setBytes(aialgorithms.proto2.RecordProto2.Bytes value) {
if (bytesBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
bytes_ = value;
onChanged();
} else {
bytesBuilder_.setMessage(value);
}
bitField0_ |= 0x00000008;
return this;
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public Builder setBytes(
aialgorithms.proto2.RecordProto2.Bytes.Builder builderForValue) {
if (bytesBuilder_ == null) {
bytes_ = builderForValue.build();
onChanged();
} else {
bytesBuilder_.setMessage(builderForValue.build());
}
bitField0_ |= 0x00000008;
return this;
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public Builder mergeBytes(aialgorithms.proto2.RecordProto2.Bytes value) {
if (bytesBuilder_ == null) {
if (((bitField0_ & 0x00000008) == 0x00000008) &&
bytes_ != aialgorithms.proto2.RecordProto2.Bytes.getDefaultInstance()) {
bytes_ =
aialgorithms.proto2.RecordProto2.Bytes.newBuilder(bytes_).mergeFrom(value).buildPartial();
} else {
bytes_ = value;
}
onChanged();
} else {
bytesBuilder_.mergeFrom(value);
}
bitField0_ |= 0x00000008;
return this;
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public Builder clearBytes() {
if (bytesBuilder_ == null) {
bytes_ = aialgorithms.proto2.RecordProto2.Bytes.getDefaultInstance();
onChanged();
} else {
bytesBuilder_.clear();
}
bitField0_ = (bitField0_ & ~0x00000008);
return this;
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public aialgorithms.proto2.RecordProto2.Bytes.Builder getBytesBuilder() {
bitField0_ |= 0x00000008;
onChanged();
return getBytesFieldBuilder().getBuilder();
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
public aialgorithms.proto2.RecordProto2.BytesOrBuilder getBytesOrBuilder() {
if (bytesBuilder_ != null) {
return bytesBuilder_.getMessageOrBuilder();
} else {
return bytes_;
}
}
/**
* optional .aialgorithms.proto2.Bytes bytes = 9;
*/
private com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Bytes, aialgorithms.proto2.RecordProto2.Bytes.Builder, aialgorithms.proto2.RecordProto2.BytesOrBuilder>
getBytesFieldBuilder() {
if (bytesBuilder_ == null) {
bytesBuilder_ = new com.google.protobuf.SingleFieldBuilder<
aialgorithms.proto2.RecordProto2.Bytes, aialgorithms.proto2.RecordProto2.Bytes.Builder, aialgorithms.proto2.RecordProto2.BytesOrBuilder>(
bytes_,
getParentForChildren(),
isClean());
bytes_ = null;
}
return bytesBuilder_;
}
// @@protoc_insertion_point(builder_scope:aialgorithms.proto2.Value)
}
static {
defaultInstance = new Value(true);
defaultInstance.initFields();
}
// @@protoc_insertion_point(class_scope:aialgorithms.proto2.Value)
}
public interface MapEntryOrBuilder
extends com.google.protobuf.MessageOrBuilder {
// optional string key = 1;
/**
* optional string key = 1;
*/
boolean hasKey();
/**
* optional string key = 1;
*/
java.lang.String getKey();
/**
* optional string key = 1;
*/
com.google.protobuf.ByteString
getKeyBytes();
// optional .aialgorithms.proto2.Value value = 2;
/**
* optional .aialgorithms.proto2.Value value = 2;
*/
boolean hasValue();
/**
* optional .aialgorithms.proto2.Value value = 2;
*/
aialgorithms.proto2.RecordProto2.Value getValue();
/**
* optional .aialgorithms.proto2.Value value = 2;
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com.google.protobuf.CodedInputStream input,
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public static Builder newBuilder(aialgorithms.proto2.RecordProto2.MapEntry prototype) {
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com.google.protobuf.GeneratedMessage.BuilderParent parent) {
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*/
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*/
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if (valueBuilder_ != null) {
return valueBuilder_.getMessageOrBuilder();
} else {
return value_;
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* optional .aialgorithms.proto2.Value value = 2;
*/
private com.google.protobuf.SingleFieldBuilder<
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getValueFieldBuilder() {
if (valueBuilder_ == null) {
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static {
defaultInstance = new MapEntry(true);
defaultInstance.initFields();
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// @@protoc_insertion_point(class_scope:aialgorithms.proto2.MapEntry)
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public interface RecordOrBuilder
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// repeated .aialgorithms.proto2.MapEntry features = 1;
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
java.util.Listrepeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
aialgorithms.proto2.RecordProto2.MapEntry getFeatures(int index);
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
int getFeaturesCount();
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
java.util.List
getFeaturesOrBuilderList();
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
aialgorithms.proto2.RecordProto2.MapEntryOrBuilder getFeaturesOrBuilder(
int index);
// repeated .aialgorithms.proto2.MapEntry label = 2;
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
java.util.Listrepeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
aialgorithms.proto2.RecordProto2.MapEntry getLabel(int index);
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
int getLabelCount();
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
java.util.List
getLabelOrBuilderList();
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
aialgorithms.proto2.RecordProto2.MapEntryOrBuilder getLabelOrBuilder(
int index);
// optional string uid = 3;
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
boolean hasUid();
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
java.lang.String getUid();
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
com.google.protobuf.ByteString
getUidBytes();
// optional string metadata = 4;
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
boolean hasMetadata();
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
java.lang.String getMetadata();
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
com.google.protobuf.ByteString
getMetadataBytes();
// optional string configuration = 5;
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
boolean hasConfiguration();
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
java.lang.String getConfiguration();
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
com.google.protobuf.ByteString
getConfigurationBytes();
}
/**
* Protobuf type {@code aialgorithms.proto2.Record}
*/
public static final class Record extends
com.google.protobuf.GeneratedMessage
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// Use Record.newBuilder() to construct.
private Record(com.google.protobuf.GeneratedMessage.Builder builder) {
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this.unknownFields = builder.getUnknownFields();
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private Record(boolean noInit) { this.unknownFields = com.google.protobuf.UnknownFieldSet.getDefaultInstance(); }
private static final Record defaultInstance;
public static Record getDefaultInstance() {
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public Record getDefaultInstanceForType() {
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private final com.google.protobuf.UnknownFieldSet unknownFields;
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return this.unknownFields;
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private Record(
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initFields();
int mutable_bitField0_ = 0;
com.google.protobuf.UnknownFieldSet.Builder unknownFields =
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try {
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if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) {
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*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public java.util.Listrepeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public java.util.List
getFeaturesOrBuilderList() {
return features_;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public int getFeaturesCount() {
return features_.size();
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry getFeatures(int index) {
return features_.get(index);
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public aialgorithms.proto2.RecordProto2.MapEntryOrBuilder getFeaturesOrBuilder(
int index) {
return features_.get(index);
}
// repeated .aialgorithms.proto2.MapEntry label = 2;
public static final int LABEL_FIELD_NUMBER = 2;
private java.util.Listrepeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public java.util.Listrepeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public java.util.List
getLabelOrBuilderList() {
return label_;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public int getLabelCount() {
return label_.size();
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry getLabel(int index) {
return label_.get(index);
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public aialgorithms.proto2.RecordProto2.MapEntryOrBuilder getLabelOrBuilder(
int index) {
return label_.get(index);
}
// optional string uid = 3;
public static final int UID_FIELD_NUMBER = 3;
private java.lang.Object uid_;
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public boolean hasUid() {
return ((bitField0_ & 0x00000001) == 0x00000001);
}
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public java.lang.String getUid() {
java.lang.Object ref = uid_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs =
(com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
if (bs.isValidUtf8()) {
uid_ = s;
}
return s;
}
}
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public com.google.protobuf.ByteString
getUidBytes() {
java.lang.Object ref = uid_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
uid_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
// optional string metadata = 4;
public static final int METADATA_FIELD_NUMBER = 4;
private java.lang.Object metadata_;
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public boolean hasMetadata() {
return ((bitField0_ & 0x00000002) == 0x00000002);
}
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public java.lang.String getMetadata() {
java.lang.Object ref = metadata_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs =
(com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
if (bs.isValidUtf8()) {
metadata_ = s;
}
return s;
}
}
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public com.google.protobuf.ByteString
getMetadataBytes() {
java.lang.Object ref = metadata_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
metadata_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
// optional string configuration = 5;
public static final int CONFIGURATION_FIELD_NUMBER = 5;
private java.lang.Object configuration_;
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public boolean hasConfiguration() {
return ((bitField0_ & 0x00000004) == 0x00000004);
}
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public java.lang.String getConfiguration() {
java.lang.Object ref = configuration_;
if (ref instanceof java.lang.String) {
return (java.lang.String) ref;
} else {
com.google.protobuf.ByteString bs =
(com.google.protobuf.ByteString) ref;
java.lang.String s = bs.toStringUtf8();
if (bs.isValidUtf8()) {
configuration_ = s;
}
return s;
}
}
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public com.google.protobuf.ByteString
getConfigurationBytes() {
java.lang.Object ref = configuration_;
if (ref instanceof java.lang.String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
configuration_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
private void initFields() {
features_ = java.util.Collections.emptyList();
label_ = java.util.Collections.emptyList();
uid_ = "";
metadata_ = "";
configuration_ = "";
}
private byte memoizedIsInitialized = -1;
public final boolean isInitialized() {
byte isInitialized = memoizedIsInitialized;
if (isInitialized != -1) return isInitialized == 1;
memoizedIsInitialized = 1;
return true;
}
public void writeTo(com.google.protobuf.CodedOutputStream output)
throws java.io.IOException {
getSerializedSize();
for (int i = 0; i < features_.size(); i++) {
output.writeMessage(1, features_.get(i));
}
for (int i = 0; i < label_.size(); i++) {
output.writeMessage(2, label_.get(i));
}
if (((bitField0_ & 0x00000001) == 0x00000001)) {
output.writeBytes(3, getUidBytes());
}
if (((bitField0_ & 0x00000002) == 0x00000002)) {
output.writeBytes(4, getMetadataBytes());
}
if (((bitField0_ & 0x00000004) == 0x00000004)) {
output.writeBytes(5, getConfigurationBytes());
}
getUnknownFields().writeTo(output);
}
private int memoizedSerializedSize = -1;
public int getSerializedSize() {
int size = memoizedSerializedSize;
if (size != -1) return size;
size = 0;
for (int i = 0; i < features_.size(); i++) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(1, features_.get(i));
}
for (int i = 0; i < label_.size(); i++) {
size += com.google.protobuf.CodedOutputStream
.computeMessageSize(2, label_.get(i));
}
if (((bitField0_ & 0x00000001) == 0x00000001)) {
size += com.google.protobuf.CodedOutputStream
.computeBytesSize(3, getUidBytes());
}
if (((bitField0_ & 0x00000002) == 0x00000002)) {
size += com.google.protobuf.CodedOutputStream
.computeBytesSize(4, getMetadataBytes());
}
if (((bitField0_ & 0x00000004) == 0x00000004)) {
size += com.google.protobuf.CodedOutputStream
.computeBytesSize(5, getConfigurationBytes());
}
size += getUnknownFields().getSerializedSize();
memoizedSerializedSize = size;
return size;
}
private static final long serialVersionUID = 0L;
@java.lang.Override
protected java.lang.Object writeReplace()
throws java.io.ObjectStreamException {
return super.writeReplace();
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Record parseDelimitedFrom(java.io.InputStream input)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Record parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseDelimitedFrom(input, extensionRegistry);
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return PARSER.parseFrom(input);
}
public static aialgorithms.proto2.RecordProto2.Record parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return PARSER.parseFrom(input, extensionRegistry);
}
public static Builder newBuilder() { return Builder.create(); }
public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder(aialgorithms.proto2.RecordProto2.Record prototype) {
return newBuilder().mergeFrom(prototype);
}
public Builder toBuilder() { return newBuilder(this); }
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessage.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
* Protobuf type {@code aialgorithms.proto2.Record}
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessage.Builderrepeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public java.util.Listrepeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public int getFeaturesCount() {
if (featuresBuilder_ == null) {
return features_.size();
} else {
return featuresBuilder_.getCount();
}
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry getFeatures(int index) {
if (featuresBuilder_ == null) {
return features_.get(index);
} else {
return featuresBuilder_.getMessage(index);
}
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder setFeatures(
int index, aialgorithms.proto2.RecordProto2.MapEntry value) {
if (featuresBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureFeaturesIsMutable();
features_.set(index, value);
onChanged();
} else {
featuresBuilder_.setMessage(index, value);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder setFeatures(
int index, aialgorithms.proto2.RecordProto2.MapEntry.Builder builderForValue) {
if (featuresBuilder_ == null) {
ensureFeaturesIsMutable();
features_.set(index, builderForValue.build());
onChanged();
} else {
featuresBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder addFeatures(aialgorithms.proto2.RecordProto2.MapEntry value) {
if (featuresBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureFeaturesIsMutable();
features_.add(value);
onChanged();
} else {
featuresBuilder_.addMessage(value);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder addFeatures(
int index, aialgorithms.proto2.RecordProto2.MapEntry value) {
if (featuresBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureFeaturesIsMutable();
features_.add(index, value);
onChanged();
} else {
featuresBuilder_.addMessage(index, value);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder addFeatures(
aialgorithms.proto2.RecordProto2.MapEntry.Builder builderForValue) {
if (featuresBuilder_ == null) {
ensureFeaturesIsMutable();
features_.add(builderForValue.build());
onChanged();
} else {
featuresBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder addFeatures(
int index, aialgorithms.proto2.RecordProto2.MapEntry.Builder builderForValue) {
if (featuresBuilder_ == null) {
ensureFeaturesIsMutable();
features_.add(index, builderForValue.build());
onChanged();
} else {
featuresBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder addAllFeatures(
java.lang.Iterable values) {
if (featuresBuilder_ == null) {
ensureFeaturesIsMutable();
super.addAll(values, features_);
onChanged();
} else {
featuresBuilder_.addAllMessages(values);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder clearFeatures() {
if (featuresBuilder_ == null) {
features_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000001);
onChanged();
} else {
featuresBuilder_.clear();
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public Builder removeFeatures(int index) {
if (featuresBuilder_ == null) {
ensureFeaturesIsMutable();
features_.remove(index);
onChanged();
} else {
featuresBuilder_.remove(index);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry.Builder getFeaturesBuilder(
int index) {
return getFeaturesFieldBuilder().getBuilder(index);
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public aialgorithms.proto2.RecordProto2.MapEntryOrBuilder getFeaturesOrBuilder(
int index) {
if (featuresBuilder_ == null) {
return features_.get(index); } else {
return featuresBuilder_.getMessageOrBuilder(index);
}
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public java.util.List
getFeaturesOrBuilderList() {
if (featuresBuilder_ != null) {
return featuresBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(features_);
}
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry.Builder addFeaturesBuilder() {
return getFeaturesFieldBuilder().addBuilder(
aialgorithms.proto2.RecordProto2.MapEntry.getDefaultInstance());
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry.Builder addFeaturesBuilder(
int index) {
return getFeaturesFieldBuilder().addBuilder(
index, aialgorithms.proto2.RecordProto2.MapEntry.getDefaultInstance());
}
/**
* repeated .aialgorithms.proto2.MapEntry features = 1;
*
*
* Map from the name of the feature to the value.
*
* For vectors and libsvm-like datasets,
* a single feature with the name `values`
* should be specified.
*
*/
public java.util.Listrepeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public java.util.Listrepeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public int getLabelCount() {
if (labelBuilder_ == null) {
return label_.size();
} else {
return labelBuilder_.getCount();
}
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry getLabel(int index) {
if (labelBuilder_ == null) {
return label_.get(index);
} else {
return labelBuilder_.getMessage(index);
}
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder setLabel(
int index, aialgorithms.proto2.RecordProto2.MapEntry value) {
if (labelBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureLabelIsMutable();
label_.set(index, value);
onChanged();
} else {
labelBuilder_.setMessage(index, value);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder setLabel(
int index, aialgorithms.proto2.RecordProto2.MapEntry.Builder builderForValue) {
if (labelBuilder_ == null) {
ensureLabelIsMutable();
label_.set(index, builderForValue.build());
onChanged();
} else {
labelBuilder_.setMessage(index, builderForValue.build());
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder addLabel(aialgorithms.proto2.RecordProto2.MapEntry value) {
if (labelBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureLabelIsMutable();
label_.add(value);
onChanged();
} else {
labelBuilder_.addMessage(value);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder addLabel(
int index, aialgorithms.proto2.RecordProto2.MapEntry value) {
if (labelBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
ensureLabelIsMutable();
label_.add(index, value);
onChanged();
} else {
labelBuilder_.addMessage(index, value);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder addLabel(
aialgorithms.proto2.RecordProto2.MapEntry.Builder builderForValue) {
if (labelBuilder_ == null) {
ensureLabelIsMutable();
label_.add(builderForValue.build());
onChanged();
} else {
labelBuilder_.addMessage(builderForValue.build());
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder addLabel(
int index, aialgorithms.proto2.RecordProto2.MapEntry.Builder builderForValue) {
if (labelBuilder_ == null) {
ensureLabelIsMutable();
label_.add(index, builderForValue.build());
onChanged();
} else {
labelBuilder_.addMessage(index, builderForValue.build());
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder addAllLabel(
java.lang.Iterable values) {
if (labelBuilder_ == null) {
ensureLabelIsMutable();
super.addAll(values, label_);
onChanged();
} else {
labelBuilder_.addAllMessages(values);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder clearLabel() {
if (labelBuilder_ == null) {
label_ = java.util.Collections.emptyList();
bitField0_ = (bitField0_ & ~0x00000002);
onChanged();
} else {
labelBuilder_.clear();
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public Builder removeLabel(int index) {
if (labelBuilder_ == null) {
ensureLabelIsMutable();
label_.remove(index);
onChanged();
} else {
labelBuilder_.remove(index);
}
return this;
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry.Builder getLabelBuilder(
int index) {
return getLabelFieldBuilder().getBuilder(index);
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public aialgorithms.proto2.RecordProto2.MapEntryOrBuilder getLabelOrBuilder(
int index) {
if (labelBuilder_ == null) {
return label_.get(index); } else {
return labelBuilder_.getMessageOrBuilder(index);
}
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public java.util.List
getLabelOrBuilderList() {
if (labelBuilder_ != null) {
return labelBuilder_.getMessageOrBuilderList();
} else {
return java.util.Collections.unmodifiableList(label_);
}
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry.Builder addLabelBuilder() {
return getLabelFieldBuilder().addBuilder(
aialgorithms.proto2.RecordProto2.MapEntry.getDefaultInstance());
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public aialgorithms.proto2.RecordProto2.MapEntry.Builder addLabelBuilder(
int index) {
return getLabelFieldBuilder().addBuilder(
index, aialgorithms.proto2.RecordProto2.MapEntry.getDefaultInstance());
}
/**
* repeated .aialgorithms.proto2.MapEntry label = 2;
*
*
* Optional set of labels for this record.
* Similar to features field above, the key used for
* generic scalar / vector labels should ve 'values'
*
*/
public java.util.Listoptional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public boolean hasUid() {
return ((bitField0_ & 0x00000004) == 0x00000004);
}
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public java.lang.String getUid() {
java.lang.Object ref = uid_;
if (!(ref instanceof java.lang.String)) {
java.lang.String s = ((com.google.protobuf.ByteString) ref)
.toStringUtf8();
uid_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public com.google.protobuf.ByteString
getUidBytes() {
java.lang.Object ref = uid_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
uid_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public Builder setUid(
java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000004;
uid_ = value;
onChanged();
return this;
}
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public Builder clearUid() {
bitField0_ = (bitField0_ & ~0x00000004);
uid_ = getDefaultInstance().getUid();
onChanged();
return this;
}
/**
* optional string uid = 3;
*
*
* Unique identifier for this record in the dataset.
*
* Whilst not necessary, this allows better
* debugging where there are data issues.
*
* This is not used by the algorithm directly.
*
*/
public Builder setUidBytes(
com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000004;
uid_ = value;
onChanged();
return this;
}
// optional string metadata = 4;
private java.lang.Object metadata_ = "";
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public boolean hasMetadata() {
return ((bitField0_ & 0x00000008) == 0x00000008);
}
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public java.lang.String getMetadata() {
java.lang.Object ref = metadata_;
if (!(ref instanceof java.lang.String)) {
java.lang.String s = ((com.google.protobuf.ByteString) ref)
.toStringUtf8();
metadata_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public com.google.protobuf.ByteString
getMetadataBytes() {
java.lang.Object ref = metadata_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
metadata_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public Builder setMetadata(
java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000008;
metadata_ = value;
onChanged();
return this;
}
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public Builder clearMetadata() {
bitField0_ = (bitField0_ & ~0x00000008);
metadata_ = getDefaultInstance().getMetadata();
onChanged();
return this;
}
/**
* optional string metadata = 4;
*
*
* Textual metadata describing the record.
*
* This may include JSON-serialized information
* about the source of the record.
*
* This is not used by the algorithm directly.
*
*/
public Builder setMetadataBytes(
com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000008;
metadata_ = value;
onChanged();
return this;
}
// optional string configuration = 5;
private java.lang.Object configuration_ = "";
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public boolean hasConfiguration() {
return ((bitField0_ & 0x00000010) == 0x00000010);
}
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public java.lang.String getConfiguration() {
java.lang.Object ref = configuration_;
if (!(ref instanceof java.lang.String)) {
java.lang.String s = ((com.google.protobuf.ByteString) ref)
.toStringUtf8();
configuration_ = s;
return s;
} else {
return (java.lang.String) ref;
}
}
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public com.google.protobuf.ByteString
getConfigurationBytes() {
java.lang.Object ref = configuration_;
if (ref instanceof String) {
com.google.protobuf.ByteString b =
com.google.protobuf.ByteString.copyFromUtf8(
(java.lang.String) ref);
configuration_ = b;
return b;
} else {
return (com.google.protobuf.ByteString) ref;
}
}
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public Builder setConfiguration(
java.lang.String value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000010;
configuration_ = value;
onChanged();
return this;
}
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public Builder clearConfiguration() {
bitField0_ = (bitField0_ & ~0x00000010);
configuration_ = getDefaultInstance().getConfiguration();
onChanged();
return this;
}
/**
* optional string configuration = 5;
*
*
* Optional serialized JSON object that allows per-record
* hyper-parameters/configuration/other information to be set.
*
* The meaning/interpretation of this field is defined by
* the algorithm author and may not be supported.
*
* This is used to pass additional inference configuration
* when batch inference is used (e.g. types of scores to return).
*
*/
public Builder setConfigurationBytes(
com.google.protobuf.ByteString value) {
if (value == null) {
throw new NullPointerException();
}
bitField0_ |= 0x00000010;
configuration_ = value;
onChanged();
return this;
}
// @@protoc_insertion_point(builder_scope:aialgorithms.proto2.Record)
}
static {
defaultInstance = new Record(true);
defaultInstance.initFields();
}
// @@protoc_insertion_point(class_scope:aialgorithms.proto2.Record)
}
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