Class EvaluationJobConfig (0.122.11)

public final class EvaluationJobConfig extends GeneratedMessageV3 implements EvaluationJobConfigOrBuilder

Configures specific details of how a continuous evaluation job works. Provide this configuration when you create an EvaluationJob.

Protobuf type google.cloud.datalabeling.v1beta1.EvaluationJobConfig

Static Fields

BIGQUERY_IMPORT_KEYS_FIELD_NUMBER

public static final int BIGQUERY_IMPORT_KEYS_FIELD_NUMBER
Field Value
TypeDescription
int

BOUNDING_POLY_CONFIG_FIELD_NUMBER

public static final int BOUNDING_POLY_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

EVALUATION_CONFIG_FIELD_NUMBER

public static final int EVALUATION_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

EVALUATION_JOB_ALERT_CONFIG_FIELD_NUMBER

public static final int EVALUATION_JOB_ALERT_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

EXAMPLE_COUNT_FIELD_NUMBER

public static final int EXAMPLE_COUNT_FIELD_NUMBER
Field Value
TypeDescription
int

EXAMPLE_SAMPLE_PERCENTAGE_FIELD_NUMBER

public static final int EXAMPLE_SAMPLE_PERCENTAGE_FIELD_NUMBER
Field Value
TypeDescription
int

HUMAN_ANNOTATION_CONFIG_FIELD_NUMBER

public static final int HUMAN_ANNOTATION_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

IMAGE_CLASSIFICATION_CONFIG_FIELD_NUMBER

public static final int IMAGE_CLASSIFICATION_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

INPUT_CONFIG_FIELD_NUMBER

public static final int INPUT_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

TEXT_CLASSIFICATION_CONFIG_FIELD_NUMBER

public static final int TEXT_CLASSIFICATION_CONFIG_FIELD_NUMBER
Field Value
TypeDescription
int

Static Methods

getDefaultInstance()

public static EvaluationJobConfig getDefaultInstance()
Returns
TypeDescription
EvaluationJobConfig

getDescriptor()

public static final Descriptors.Descriptor getDescriptor()
Returns
TypeDescription
Descriptor

newBuilder()

public static EvaluationJobConfig.Builder newBuilder()
Returns
TypeDescription
EvaluationJobConfig.Builder

newBuilder(EvaluationJobConfig prototype)

public static EvaluationJobConfig.Builder newBuilder(EvaluationJobConfig prototype)
Parameter
NameDescription
prototypeEvaluationJobConfig
Returns
TypeDescription
EvaluationJobConfig.Builder

parseDelimitedFrom(InputStream input)

public static EvaluationJobConfig parseDelimitedFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
IOException

parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static EvaluationJobConfig parseDelimitedFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
IOException

parseFrom(byte[] data)

public static EvaluationJobConfig parseFrom(byte[] data)
Parameter
NameDescription
databyte[]
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)

public static EvaluationJobConfig parseFrom(byte[] data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
databyte[]
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data)

public static EvaluationJobConfig parseFrom(ByteString data)
Parameter
NameDescription
dataByteString
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)

public static EvaluationJobConfig parseFrom(ByteString data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteString
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(CodedInputStream input)

public static EvaluationJobConfig parseFrom(CodedInputStream input)
Parameter
NameDescription
inputCodedInputStream
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
IOException

parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)

public static EvaluationJobConfig parseFrom(CodedInputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputCodedInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
IOException

parseFrom(InputStream input)

public static EvaluationJobConfig parseFrom(InputStream input)
Parameter
NameDescription
inputInputStream
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
IOException

parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)

public static EvaluationJobConfig parseFrom(InputStream input, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
inputInputStream
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
IOException

parseFrom(ByteBuffer data)

public static EvaluationJobConfig parseFrom(ByteBuffer data)
Parameter
NameDescription
dataByteBuffer
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)

public static EvaluationJobConfig parseFrom(ByteBuffer data, ExtensionRegistryLite extensionRegistry)
Parameters
NameDescription
dataByteBuffer
extensionRegistryExtensionRegistryLite
Returns
TypeDescription
EvaluationJobConfig
Exceptions
TypeDescription
InvalidProtocolBufferException

parser()

public static Parser<EvaluationJobConfig> parser()
Returns
TypeDescription
Parser<EvaluationJobConfig>

Methods

containsBigqueryImportKeys(String key)

public boolean containsBigqueryImportKeys(String key)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

  • data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
  • reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
  • label_json_key: the label key for prediction output. Required.
  • label_score_json_key: the score key for prediction output. Required.
  • bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameter
NameDescription
keyString
Returns
TypeDescription
boolean

equals(Object obj)

public boolean equals(Object obj)
Parameter
NameDescription
objObject
Returns
TypeDescription
boolean
Overrides

getBigqueryImportKeys()

public Map<String,String> getBigqueryImportKeys()
Returns
TypeDescription
Map<String,String>

getBigqueryImportKeysCount()

public int getBigqueryImportKeysCount()

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

  • data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
  • reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
  • label_json_key: the label key for prediction output. Required.
  • label_score_json_key: the score key for prediction output. Required.
  • bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Returns
TypeDescription
int

getBigqueryImportKeysMap()

public Map<String,String> getBigqueryImportKeysMap()

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

  • data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
  • reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
  • label_json_key: the label key for prediction output. Required.
  • label_score_json_key: the score key for prediction output. Required.
  • bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Returns
TypeDescription
Map<String,String>

getBigqueryImportKeysOrDefault(String key, String defaultValue)

public String getBigqueryImportKeysOrDefault(String key, String defaultValue)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

  • data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
  • reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
  • label_json_key: the label key for prediction output. Required.
  • label_score_json_key: the score key for prediction output. Required.
  • bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameters
NameDescription
keyString
defaultValueString
Returns
TypeDescription
String

getBigqueryImportKeysOrThrow(String key)

public String getBigqueryImportKeysOrThrow(String key)

Required. Prediction keys that tell Data Labeling Service where to find the data for evaluation in your BigQuery table. When the service samples prediction input and output from your model version and saves it to BigQuery, the data gets stored as JSON strings in the BigQuery table. These keys tell Data Labeling Service how to parse the JSON. You can provide the following entries in this field:

  • data_json_key: the data key for prediction input. You must provide either this key or reference_json_key.
  • reference_json_key: the data reference key for prediction input. You must provide either this key or data_json_key.
  • label_json_key: the label key for prediction output. Required.
  • label_score_json_key: the score key for prediction output. Required.
  • bounding_box_json_key: the bounding box key for prediction output. Required if your model version perform image object detection. Learn how to configure prediction keys.

map<string, string> bigquery_import_keys = 9;

Parameter
NameDescription
keyString
Returns
TypeDescription
String

getBoundingPolyConfig()

public BoundingPolyConfig getBoundingPolyConfig()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns
TypeDescription
BoundingPolyConfig

The boundingPolyConfig.

getBoundingPolyConfigOrBuilder()

public BoundingPolyConfigOrBuilder getBoundingPolyConfigOrBuilder()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns
TypeDescription
BoundingPolyConfigOrBuilder

getDefaultInstanceForType()

public EvaluationJobConfig getDefaultInstanceForType()
Returns
TypeDescription
EvaluationJobConfig

getEvaluationConfig()

public EvaluationConfig getEvaluationConfig()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns
TypeDescription
EvaluationConfig

The evaluationConfig.

getEvaluationConfigOrBuilder()

public EvaluationConfigOrBuilder getEvaluationConfigOrBuilder()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns
TypeDescription
EvaluationConfigOrBuilder

getEvaluationJobAlertConfig()

public EvaluationJobAlertConfig getEvaluationJobAlertConfig()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns
TypeDescription
EvaluationJobAlertConfig

The evaluationJobAlertConfig.

getEvaluationJobAlertConfigOrBuilder()

public EvaluationJobAlertConfigOrBuilder getEvaluationJobAlertConfigOrBuilder()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns
TypeDescription
EvaluationJobAlertConfigOrBuilder

getExampleCount()

public int getExampleCount()

Required. The maximum number of predictions to sample and save to BigQuery during each evaluation interval. This limit overrides example_sample_percentage: even if the service has not sampled enough predictions to fulfill example_sample_perecentage during an interval, it stops sampling predictions when it meets this limit.

int32 example_count = 10;

Returns
TypeDescription
int

The exampleCount.

getExampleSamplePercentage()

public double getExampleSamplePercentage()

Required. Fraction of predictions to sample and save to BigQuery during each evaluation interval. For example, 0.1 means 10% of predictions served by your model version get saved to BigQuery.

double example_sample_percentage = 11;

Returns
TypeDescription
double

The exampleSamplePercentage.

getHumanAnnotationConfig()

public HumanAnnotationConfig getHumanAnnotationConfig()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns
TypeDescription
HumanAnnotationConfig

The humanAnnotationConfig.

getHumanAnnotationConfigOrBuilder()

public HumanAnnotationConfigOrBuilder getHumanAnnotationConfigOrBuilder()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns
TypeDescription
HumanAnnotationConfigOrBuilder

getHumanAnnotationRequestConfigCase()

public EvaluationJobConfig.HumanAnnotationRequestConfigCase getHumanAnnotationRequestConfigCase()
Returns
TypeDescription
EvaluationJobConfig.HumanAnnotationRequestConfigCase

getImageClassificationConfig()

public ImageClassificationConfig getImageClassificationConfig()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns
TypeDescription
ImageClassificationConfig

The imageClassificationConfig.

getImageClassificationConfigOrBuilder()

public ImageClassificationConfigOrBuilder getImageClassificationConfigOrBuilder()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns
TypeDescription
ImageClassificationConfigOrBuilder

getInputConfig()

public InputConfig getInputConfig()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

  • dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
  • annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
  • If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
  • You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns
TypeDescription
InputConfig

The inputConfig.

getInputConfigOrBuilder()

public InputConfigOrBuilder getInputConfigOrBuilder()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

  • dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
  • annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
  • If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
  • You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns
TypeDescription
InputConfigOrBuilder

getParserForType()

public Parser<EvaluationJobConfig> getParserForType()
Returns
TypeDescription
Parser<EvaluationJobConfig>
Overrides

getSerializedSize()

public int getSerializedSize()
Returns
TypeDescription
int
Overrides

getTextClassificationConfig()

public TextClassificationConfig getTextClassificationConfig()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns
TypeDescription
TextClassificationConfig

The textClassificationConfig.

getTextClassificationConfigOrBuilder()

public TextClassificationConfigOrBuilder getTextClassificationConfigOrBuilder()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns
TypeDescription
TextClassificationConfigOrBuilder

getUnknownFields()

public final UnknownFieldSet getUnknownFields()
Returns
TypeDescription
UnknownFieldSet
Overrides

hasBoundingPolyConfig()

public boolean hasBoundingPolyConfig()

Specify this field if your model version performs image object detection (bounding box detection). annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet.

.google.cloud.datalabeling.v1beta1.BoundingPolyConfig bounding_poly_config = 5;

Returns
TypeDescription
boolean

Whether the boundingPolyConfig field is set.

hasEvaluationConfig()

public boolean hasEvaluationConfig()

Required. Details for calculating evaluation metrics and creating Evaulations. If your model version performs image object detection, you must specify the boundingBoxEvaluationOptions field within this configuration. Otherwise, provide an empty object for this configuration.

.google.cloud.datalabeling.v1beta1.EvaluationConfig evaluation_config = 2;

Returns
TypeDescription
boolean

Whether the evaluationConfig field is set.

hasEvaluationJobAlertConfig()

public boolean hasEvaluationJobAlertConfig()

Optional. Configuration details for evaluation job alerts. Specify this field if you want to receive email alerts if the evaluation job finds that your predictions have low mean average precision during a run.

.google.cloud.datalabeling.v1beta1.EvaluationJobAlertConfig evaluation_job_alert_config = 13;

Returns
TypeDescription
boolean

Whether the evaluationJobAlertConfig field is set.

hasHumanAnnotationConfig()

public boolean hasHumanAnnotationConfig()

Optional. Details for human annotation of your data. If you set labelMissingGroundTruth to true for this evaluation job, then you must specify this field. If you plan to provide your own ground truth labels, then omit this field. Note that you must create an Instruction resource before you can specify this field. Provide the name of the instruction resource in the instruction field within this configuration.

.google.cloud.datalabeling.v1beta1.HumanAnnotationConfig human_annotation_config = 3;

Returns
TypeDescription
boolean

Whether the humanAnnotationConfig field is set.

hasImageClassificationConfig()

public boolean hasImageClassificationConfig()

Specify this field if your model version performs image classification or general classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.ImageClassificationConfig image_classification_config = 4;

Returns
TypeDescription
boolean

Whether the imageClassificationConfig field is set.

hasInputConfig()

public boolean hasInputConfig()

Rquired. Details for the sampled prediction input. Within this configuration, there are requirements for several fields:

  • dataType must be one of IMAGE, TEXT, or GENERAL_DATA.
  • annotationType must be one of IMAGE_CLASSIFICATION_ANNOTATION, TEXT_CLASSIFICATION_ANNOTATION, GENERAL_CLASSIFICATION_ANNOTATION, or IMAGE_BOUNDING_BOX_ANNOTATION (image object detection).
  • If your machine learning model performs classification, you must specify classificationMetadata.isMultiLabel.
  • You must specify bigquerySource (not gcsSource).

.google.cloud.datalabeling.v1beta1.InputConfig input_config = 1;

Returns
TypeDescription
boolean

Whether the inputConfig field is set.

hasTextClassificationConfig()

public boolean hasTextClassificationConfig()

Specify this field if your model version performs text classification. annotationSpecSet in this configuration must match EvaluationJob.annotationSpecSet. allowMultiLabel in this configuration must match classificationMetadata.isMultiLabel in input_config.

.google.cloud.datalabeling.v1beta1.TextClassificationConfig text_classification_config = 8;

Returns
TypeDescription
boolean

Whether the textClassificationConfig field is set.

hashCode()

public int hashCode()
Returns
TypeDescription
int
Overrides

internalGetFieldAccessorTable()

protected GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
Returns
TypeDescription
FieldAccessorTable
Overrides

internalGetMapField(int number)

protected MapField internalGetMapField(int number)
Parameter
NameDescription
numberint
Returns
TypeDescription
MapField
Overrides

isInitialized()

public final boolean isInitialized()
Returns
TypeDescription
boolean
Overrides

newBuilderForType()

public EvaluationJobConfig.Builder newBuilderForType()
Returns
TypeDescription
EvaluationJobConfig.Builder

newBuilderForType(GeneratedMessageV3.BuilderParent parent)

protected EvaluationJobConfig.Builder newBuilderForType(GeneratedMessageV3.BuilderParent parent)
Parameter
NameDescription
parentBuilderParent
Returns
TypeDescription
EvaluationJobConfig.Builder
Overrides

newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)

protected Object newInstance(GeneratedMessageV3.UnusedPrivateParameter unused)
Parameter
NameDescription
unusedUnusedPrivateParameter
Returns
TypeDescription
Object
Overrides

toBuilder()

public EvaluationJobConfig.Builder toBuilder()
Returns
TypeDescription
EvaluationJobConfig.Builder

writeTo(CodedOutputStream output)

public void writeTo(CodedOutputStream output)
Parameter
NameDescription
outputCodedOutputStream
Overrides Exceptions
TypeDescription
IOException