Class ImageObjectDetectionModelMetadata.Builder (2.43.0)

public static final class ImageObjectDetectionModelMetadata.Builder extends GeneratedMessageV3.Builder<ImageObjectDetectionModelMetadata.Builder> implements ImageObjectDetectionModelMetadataOrBuilder

Model metadata specific to image object detection.

Protobuf type google.cloud.automl.v1beta1.ImageObjectDetectionModelMetadata

Inheritance

Object > AbstractMessageLite.Builder<MessageType,BuilderType> > AbstractMessage.Builder<BuilderType> > GeneratedMessageV3.Builder > ImageObjectDetectionModelMetadata.Builder

Implements

ImageObjectDetectionModelMetadataOrBuilder

Inherited Members

AbstractMessage.Builder.findInitializationErrors()

AbstractMessage.Builder.getInitializationErrorString()

AbstractMessage.Builder.internalMergeFrom(AbstractMessageLite)

AbstractMessage.Builder.mergeFrom(byte[])

AbstractMessage.Builder.mergeFrom(byte[],ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(byte[],int,int)

AbstractMessage.Builder.mergeFrom(byte[],int,int,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(ByteString)

AbstractMessage.Builder.mergeFrom(ByteString,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(CodedInputStream)

AbstractMessage.Builder.mergeFrom(CodedInputStream,ExtensionRegistryLite)

AbstractMessage.Builder.mergeFrom(Message)

AbstractMessage.Builder.mergeFrom(InputStream)

AbstractMessage.Builder.mergeFrom(InputStream,ExtensionRegistryLite)

AbstractMessage.Builder.newUninitializedMessageException(Message)

AbstractMessage.Builder.toString()

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,Collection<? super T>)

AbstractMessageLite.Builder.<T>addAll(Iterable<T>,List<? super T>)

AbstractMessageLite.Builder.internalMergeFrom(MessageType)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream)

AbstractMessageLite.Builder.mergeDelimitedFrom(InputStream,ExtensionRegistryLite)

AbstractMessageLite.Builder.mergeFrom(MessageLite)

AbstractMessageLite.Builder.newUninitializedMessageException(MessageLite)

GeneratedMessageV3.Builder.addRepeatedField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.clear()

GeneratedMessageV3.Builder.clearField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.clearOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.clone()

GeneratedMessageV3.Builder.getAllFields()

GeneratedMessageV3.Builder.getDescriptorForType()

GeneratedMessageV3.Builder.getField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getFieldBuilder(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.getOneofFieldDescriptor(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.getParentForChildren()

GeneratedMessageV3.Builder.getRepeatedField(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldBuilder(Descriptors.FieldDescriptor,int)

GeneratedMessageV3.Builder.getRepeatedFieldCount(Descriptors.FieldDescriptor)

com.google.protobuf.GeneratedMessageV3.Builder.getUnknownFieldSetBuilder()

GeneratedMessageV3.Builder.getUnknownFields()

GeneratedMessageV3.Builder.hasField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.hasOneof(Descriptors.OneofDescriptor)

GeneratedMessageV3.Builder.internalGetFieldAccessorTable()

GeneratedMessageV3.Builder.internalGetMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMapFieldReflection(int)

GeneratedMessageV3.Builder.internalGetMutableMapField(int)

com.google.protobuf.GeneratedMessageV3.Builder.internalGetMutableMapFieldReflection(int)

GeneratedMessageV3.Builder.isClean()

GeneratedMessageV3.Builder.isInitialized()

GeneratedMessageV3.Builder.markClean()

GeneratedMessageV3.Builder.mergeUnknownFields(UnknownFieldSet)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownLengthDelimitedField(int,com.google.protobuf.ByteString)

com.google.protobuf.GeneratedMessageV3.Builder.mergeUnknownVarintField(int,int)

GeneratedMessageV3.Builder.newBuilderForField(Descriptors.FieldDescriptor)

GeneratedMessageV3.Builder.onBuilt()

GeneratedMessageV3.Builder.onChanged()

com.google.protobuf.GeneratedMessageV3.Builder.parseUnknownField(com.google.protobuf.CodedInputStream,com.google.protobuf.ExtensionRegistryLite,int)

GeneratedMessageV3.Builder.setField(Descriptors.FieldDescriptor,Object)

GeneratedMessageV3.Builder.setRepeatedField(Descriptors.FieldDescriptor,int,Object)

com.google.protobuf.GeneratedMessageV3.Builder.setUnknownFieldSetBuilder(com.google.protobuf.UnknownFieldSet.Builder)

GeneratedMessageV3.Builder.setUnknownFields(UnknownFieldSet)

GeneratedMessageV3.Builder.setUnknownFieldsProto3(UnknownFieldSet)

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Methods

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()

Returns
Type	Description
`Descriptor`

Methods

addRepeatedField(Descriptors.FieldDescriptor field, Object value)

public ImageObjectDetectionModelMetadata.Builder addRepeatedField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`value`	`Object`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.addRepeatedField(Descriptors.FieldDescriptor field, Object value)

build()

public ImageObjectDetectionModelMetadata build()

Returns
Type	Description
`ImageObjectDetectionModelMetadata`

buildPartial()

public ImageObjectDetectionModelMetadata buildPartial()

Returns
Type	Description
`ImageObjectDetectionModelMetadata`

clear()

public ImageObjectDetectionModelMetadata.Builder clear()

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clear()

clearField(Descriptors.FieldDescriptor field)

public ImageObjectDetectionModelMetadata.Builder clearField(Descriptors.FieldDescriptor field)

Parameter
Name	Description
`field`	`FieldDescriptor`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearField(Descriptors.FieldDescriptor field)

clearModelType()

public ImageObjectDetectionModelMetadata.Builder clearModelType()

Optional. Type of the model. The available values are:

cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

string model_type = 1;

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

clearNodeCount()

public ImageObjectDetectionModelMetadata.Builder clearNodeCount()

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.

int64 node_count = 3;

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

clearNodeQps()

public ImageObjectDetectionModelMetadata.Builder clearNodeQps()

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

double node_qps = 4;

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

clearOneof(Descriptors.OneofDescriptor oneof)

public ImageObjectDetectionModelMetadata.Builder clearOneof(Descriptors.OneofDescriptor oneof)

Parameter
Name	Description
`oneof`	`OneofDescriptor`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clearOneof(Descriptors.OneofDescriptor oneof)

clearStopReason()

public ImageObjectDetectionModelMetadata.Builder clearStopReason()

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

string stop_reason = 5;

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

clearTrainBudgetMilliNodeHours()

public ImageObjectDetectionModelMetadata.Builder clearTrainBudgetMilliNodeHours()

The train budget of creating this model, expressed in milli node hours i.e. 1,000 value in this field means 1 node hour. The actual train_cost will be equal or less than this value. If further model training ceases to provide any improvements, it will stop without using full budget and the stop_reason will be MODEL_CONVERGED. Note, node_hour = actual_hour * number_of_nodes_invovled. For model type cloud-high-accuracy-1(default) and cloud-low-latency-1, the train budget must be between 20,000 and 900,000 milli node hours, inclusive. The default value is 216, 000 which represents one day in wall time. For model type mobile-low-latency-1, mobile-versatile-1, mobile-high-accuracy-1, mobile-core-ml-low-latency-1, mobile-core-ml-versatile-1, mobile-core-ml-high-accuracy-1, the train budget must be between 1,000 and 100,000 milli node hours, inclusive. The default value is 24, 000 which represents one day in wall time.

int64 train_budget_milli_node_hours = 6;

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

clearTrainCostMilliNodeHours()

public ImageObjectDetectionModelMetadata.Builder clearTrainCostMilliNodeHours()

Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

int64 train_cost_milli_node_hours = 7;

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

clone()

public ImageObjectDetectionModelMetadata.Builder clone()

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.clone()

getDefaultInstanceForType()

public ImageObjectDetectionModelMetadata getDefaultInstanceForType()

Returns
Type	Description
`ImageObjectDetectionModelMetadata`

getDescriptorForType()

public Descriptors.Descriptor getDescriptorForType()

Returns
Type	Description
`Descriptor`

Overrides

GeneratedMessageV3.Builder<BuilderType>.getDescriptorForType()

getModelType()

public String getModelType()

Optional. Type of the model. The available values are:

cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

string model_type = 1;

Returns
Type	Description
`String`	The modelType.

getModelTypeBytes()

public ByteString getModelTypeBytes()

Optional. Type of the model. The available values are:

cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

string model_type = 1;

Returns
Type	Description
`ByteString`	The bytes for modelType.

getNodeCount()

public long getNodeCount()

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.

int64 node_count = 3;

Returns
Type	Description
`long`	The nodeCount.

getNodeQps()

public double getNodeQps()

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

double node_qps = 4;

Returns
Type	Description
`double`	The nodeQps.

getStopReason()

public String getStopReason()

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

string stop_reason = 5;

Returns
Type	Description
`String`	The stopReason.

getStopReasonBytes()

public ByteString getStopReasonBytes()

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

string stop_reason = 5;

Returns
Type	Description
`ByteString`	The bytes for stopReason.

getTrainBudgetMilliNodeHours()

public long getTrainBudgetMilliNodeHours()

int64 train_budget_milli_node_hours = 6;

Returns
Type	Description
`long`	The trainBudgetMilliNodeHours.

getTrainCostMilliNodeHours()

public long getTrainCostMilliNodeHours()

Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

int64 train_cost_milli_node_hours = 7;

Returns
Type	Description
`long`	The trainCostMilliNodeHours.

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()

Returns
Type	Description
`FieldAccessorTable`

Overrides

GeneratedMessageV3.Builder<BuilderType>.internalGetFieldAccessorTable()

isInitialized()

public final boolean isInitialized()

Returns
Type	Description
`boolean`

Overrides

GeneratedMessageV3.Builder<BuilderType>.isInitialized()

mergeFrom(ImageObjectDetectionModelMetadata other)

public ImageObjectDetectionModelMetadata.Builder mergeFrom(ImageObjectDetectionModelMetadata other)

Parameter
Name	Description
`other`	`ImageObjectDetectionModelMetadata`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public ImageObjectDetectionModelMetadata.Builder mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Parameters
Name	Description
`input`	`CodedInputStream`
`extensionRegistry`	`ExtensionRegistryLite`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

Exceptions
Type	Description
`IOException`

mergeFrom(Message other)

public ImageObjectDetectionModelMetadata.Builder mergeFrom(Message other)

Parameter
Name	Description
`other`	`Message`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

AbstractMessage.Builder<BuilderType>.mergeFrom(Message other)

mergeUnknownFields(UnknownFieldSet unknownFields)

public final ImageObjectDetectionModelMetadata.Builder mergeUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
`unknownFields`	`UnknownFieldSet`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.mergeUnknownFields(UnknownFieldSet unknownFields)

setField(Descriptors.FieldDescriptor field, Object value)

public ImageObjectDetectionModelMetadata.Builder setField(Descriptors.FieldDescriptor field, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`value`	`Object`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setField(Descriptors.FieldDescriptor field, Object value)

setModelType(String value)

public ImageObjectDetectionModelMetadata.Builder setModelType(String value)

Optional. Type of the model. The available values are:

cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

string model_type = 1;

Parameter
Name	Description
`value`	`String` The modelType to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setModelTypeBytes(ByteString value)

public ImageObjectDetectionModelMetadata.Builder setModelTypeBytes(ByteString value)

Optional. Type of the model. The available values are:

cloud-high-accuracy-1 - (default) A model to be used via prediction calls to AutoML API. Expected to have a higher latency, but should also have a higher prediction quality than other models.
cloud-low-latency-1 - A model to be used via prediction calls to AutoML API. Expected to have low latency, but may have lower prediction quality than other models.
mobile-low-latency-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have low latency, but may have lower prediction quality than other models.
mobile-versatile-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards.
mobile-high-accuracy-1 - A model that, in addition to providing prediction via AutoML API, can also be exported (see AutoMl.ExportModel) and used on a mobile or edge device with TensorFlow afterwards. Expected to have a higher latency, but should also have a higher prediction quality than other models.

string model_type = 1;

Parameter
Name	Description
`value`	`ByteString` The bytes for modelType to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setNodeCount(long value)

public ImageObjectDetectionModelMetadata.Builder setNodeCount(long value)

Output only. The number of nodes this model is deployed on. A node is an abstraction of a machine resource, which can handle online prediction QPS as given in the qps_per_node field.

int64 node_count = 3;

Parameter
Name	Description
`value`	`long` The nodeCount to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setNodeQps(double value)

public ImageObjectDetectionModelMetadata.Builder setNodeQps(double value)

Output only. An approximate number of online prediction QPS that can be supported by this model per each node on which it is deployed.

double node_qps = 4;

Parameter
Name	Description
`value`	`double` The nodeQps to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

public ImageObjectDetectionModelMetadata.Builder setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

Parameters
Name	Description
`field`	`FieldDescriptor`
`index`	`int`
`value`	`Object`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setRepeatedField(Descriptors.FieldDescriptor field, int index, Object value)

setStopReason(String value)

public ImageObjectDetectionModelMetadata.Builder setStopReason(String value)

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

string stop_reason = 5;

Parameter
Name	Description
`value`	`String` The stopReason to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setStopReasonBytes(ByteString value)

public ImageObjectDetectionModelMetadata.Builder setStopReasonBytes(ByteString value)

Output only. The reason that this create model operation stopped, e.g. BUDGET_REACHED, MODEL_CONVERGED.

string stop_reason = 5;

Parameter
Name	Description
`value`	`ByteString` The bytes for stopReason to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setTrainBudgetMilliNodeHours(long value)

public ImageObjectDetectionModelMetadata.Builder setTrainBudgetMilliNodeHours(long value)

int64 train_budget_milli_node_hours = 6;

Parameter
Name	Description
`value`	`long` The trainBudgetMilliNodeHours to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setTrainCostMilliNodeHours(long value)

public ImageObjectDetectionModelMetadata.Builder setTrainCostMilliNodeHours(long value)

Output only. The actual train cost of creating this model, expressed in milli node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed to not exceed the train budget.

int64 train_cost_milli_node_hours = 7;

Parameter
Name	Description
`value`	`long` The trainCostMilliNodeHours to set.

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`	This builder for chaining.

setUnknownFields(UnknownFieldSet unknownFields)

public final ImageObjectDetectionModelMetadata.Builder setUnknownFields(UnknownFieldSet unknownFields)

Parameter
Name	Description
`unknownFields`	`UnknownFieldSet`

Returns
Type	Description
`ImageObjectDetectionModelMetadata.Builder`

Overrides

GeneratedMessageV3.Builder<BuilderType>.setUnknownFields(UnknownFieldSet unknownFields)