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    Google Cloud Ai Platform V1 Client - Class ModelDeploymentMonitoringJob (0.10.0)

    Reference documentation and code samples for the Google Cloud Ai Platform V1 Client class ModelDeploymentMonitoringJob.

    Represents a job that runs periodically to monitor the deployed models in an endpoint. It will analyze the logged training & prediction data to detect any abnormal behaviors.

    Generated from protobuf message google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob

    Methods

    __construct

    Constructor.

    Parameters
    Name
    Description
    data
    array

    Optional. Data for populating the Message object.

    ↳ name
    string

    Output only. Resource name of a ModelDeploymentMonitoringJob.

    ↳ display_name
    string

    Required. The user-defined name of the ModelDeploymentMonitoringJob. The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.

    ↳ endpoint
    string

    Required. Endpoint resource name. Format: projects/{project}/locations/{location}/endpoints/{endpoint}

    ↳ state
    int

    Output only. The detailed state of the monitoring job. When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.

    ↳ schedule_state
    int

    Output only. Schedule state when the monitoring job is in Running state.

    ↳ latest_monitoring_pipeline_metadata
    Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob\LatestMonitoringPipelineMetadata

    Output only. Latest triggered monitoring pipeline metadata.

    ↳ model_deployment_monitoring_objective_configs
    array< Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringObjectiveConfig >

    Required. The config for monitoring objectives. This is a per DeployedModel config. Each DeployedModel needs to be configured separately.

    ↳ model_deployment_monitoring_schedule_config
    Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringScheduleConfig

    Required. Schedule config for running the monitoring job.

    ↳ logging_sampling_strategy
    Google\Cloud\AIPlatform\V1\SamplingStrategy

    Required. Sample Strategy for logging.

    ↳ model_monitoring_alert_config
    Google\Cloud\AIPlatform\V1\ModelMonitoringAlertConfig

    Alert config for model monitoring.

    ↳ predict_instance_schema_uri
    string

    YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation). If not set, we will generate predict schema from collected predict requests.

    ↳ sample_predict_instance
    Google\Protobuf\Value

    Sample Predict instance, same format as PredictRequest.instances , this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri . If not set, we will generate predict schema from collected predict requests.

    ↳ analysis_instance_schema_uri
    string

    YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze. If this field is empty, all the feature data types are inferred from predict_instance_schema_uri , meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.

    ↳ bigquery_tables
    array< Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringBigQueryTable >

    Output only. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum: 1. Training data logging predict request/response 2. Serving data logging predict request/response

    ↳ log_ttl
    Google\Protobuf\Duration

    The TTL of BigQuery tables in user projects which stores logs. A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.

    ↳ labels
    array|Google\Protobuf\Internal\MapField

    The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    ↳ create_time
    Google\Protobuf\Timestamp

    Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

    ↳ update_time
    Google\Protobuf\Timestamp

    Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

    ↳ next_schedule_time
    Google\Protobuf\Timestamp

    Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

    ↳ stats_anomalies_base_directory
    Google\Cloud\AIPlatform\V1\GcsDestination

    Stats anomalies base folder path.

    ↳ encryption_spec
    Google\Cloud\AIPlatform\V1\EncryptionSpec

    Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.

    ↳ enable_monitoring_pipeline_logs
    bool

    If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected. Please note the logs incur cost, which are subject to Cloud Logging pricing .

    ↳ error
    Google\Rpc\Status

    Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED .

    getName

    Output only. Resource name of a ModelDeploymentMonitoringJob.

    Generated from protobuf field string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    string

    setName

    Output only. Resource name of a ModelDeploymentMonitoringJob.

    Generated from protobuf field string name = 1 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    string
    Returns
    Type
    Description
    $this

    getDisplayName

    Required. The user-defined name of the ModelDeploymentMonitoringJob.

    The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.

    Generated from protobuf field string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

    Returns
    Type
    Description
    string

    setDisplayName

    Required. The user-defined name of the ModelDeploymentMonitoringJob.

    The name can be up to 128 characters long and can consist of any UTF-8 characters. Display name of a ModelDeploymentMonitoringJob.

    Generated from protobuf field string display_name = 2 [(.google.api.field_behavior) = REQUIRED];

    Parameter
    Name
    Description
    var
    string
    Returns
    Type
    Description
    $this

    getEndpoint

    Required. Endpoint resource name.

    Format: projects/{project}/locations/{location}/endpoints/{endpoint}

    Generated from protobuf field string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = {

    Returns
    Type
    Description
    string

    setEndpoint

    Required. Endpoint resource name.

    Format: projects/{project}/locations/{location}/endpoints/{endpoint}

    Generated from protobuf field string endpoint = 3 [(.google.api.field_behavior) = REQUIRED, (.google.api.resource_reference) = {

    Parameter
    Name
    Description
    var
    string
    Returns
    Type
    Description
    $this

    getState

    Output only. The detailed state of the monitoring job.

    When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.

    Generated from protobuf field .google.cloud.aiplatform.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    int

    setState

    Output only. The detailed state of the monitoring job.

    When the job is still creating, the state will be 'PENDING'. Once the job is successfully created, the state will be 'RUNNING'. Pause the job, the state will be 'PAUSED'. Resume the job, the state will return to 'RUNNING'.

    Generated from protobuf field .google.cloud.aiplatform.v1.JobState state = 4 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    int
    Returns
    Type
    Description
    $this

    getScheduleState

    Output only. Schedule state when the monitoring job is in Running state.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    int

    setScheduleState

    Output only. Schedule state when the monitoring job is in Running state.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.MonitoringScheduleState schedule_state = 5 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    int
    Returns
    Type
    Description
    $this

    getLatestMonitoringPipelineMetadata

    Output only. Latest triggered monitoring pipeline metadata.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata latest_monitoring_pipeline_metadata = 25 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob\LatestMonitoringPipelineMetadata |null

    hasLatestMonitoringPipelineMetadata

    clearLatestMonitoringPipelineMetadata

    setLatestMonitoringPipelineMetadata

    Output only. Latest triggered monitoring pipeline metadata.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringJob.LatestMonitoringPipelineMetadata latest_monitoring_pipeline_metadata = 25 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringJob\LatestMonitoringPipelineMetadata
    Returns
    Type
    Description
    $this

    getModelDeploymentMonitoringObjectiveConfigs

    Required. The config for monitoring objectives. This is a per DeployedModel config.

    Each DeployedModel needs to be configured separately.

    Generated from protobuf field repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

    Returns
    Type
    Description
    Google\Protobuf\Internal\RepeatedField

    setModelDeploymentMonitoringObjectiveConfigs

    Required. The config for monitoring objectives. This is a per DeployedModel config.

    Each DeployedModel needs to be configured separately.

    Generated from protobuf field repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringObjectiveConfig model_deployment_monitoring_objective_configs = 6 [(.google.api.field_behavior) = REQUIRED];

    Parameter
    Name
    Description
    var
    array< Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringObjectiveConfig >
    Returns
    Type
    Description
    $this

    getModelDeploymentMonitoringScheduleConfig

    Required. Schedule config for running the monitoring job.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];

    Returns
    Type
    Description
    Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringScheduleConfig |null

    hasModelDeploymentMonitoringScheduleConfig

    clearModelDeploymentMonitoringScheduleConfig

    setModelDeploymentMonitoringScheduleConfig

    Required. Schedule config for running the monitoring job.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelDeploymentMonitoringScheduleConfig model_deployment_monitoring_schedule_config = 7 [(.google.api.field_behavior) = REQUIRED];

    Parameter
    Name
    Description
    var
    Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringScheduleConfig
    Returns
    Type
    Description
    $this

    getLoggingSamplingStrategy

    Required. Sample Strategy for logging.

    Generated from protobuf field .google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];

    Returns
    Type
    Description
    Google\Cloud\AIPlatform\V1\SamplingStrategy |null

    hasLoggingSamplingStrategy

    clearLoggingSamplingStrategy

    setLoggingSamplingStrategy

    Required. Sample Strategy for logging.

    Generated from protobuf field .google.cloud.aiplatform.v1.SamplingStrategy logging_sampling_strategy = 8 [(.google.api.field_behavior) = REQUIRED];

    Parameter
    Name
    Description
    var
    Google\Cloud\AIPlatform\V1\SamplingStrategy
    Returns
    Type
    Description
    $this

    getModelMonitoringAlertConfig

    Alert config for model monitoring.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

    Returns
    Type
    Description
    Google\Cloud\AIPlatform\V1\ModelMonitoringAlertConfig |null

    hasModelMonitoringAlertConfig

    clearModelMonitoringAlertConfig

    setModelMonitoringAlertConfig

    Alert config for model monitoring.

    Generated from protobuf field .google.cloud.aiplatform.v1.ModelMonitoringAlertConfig model_monitoring_alert_config = 15;

    Parameter
    Name
    Description
    var
    Google\Cloud\AIPlatform\V1\ModelMonitoringAlertConfig
    Returns
    Type
    Description
    $this

    getPredictInstanceSchemaUri

    YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation).

    If not set, we will generate predict schema from collected predict requests.

    Generated from protobuf field string predict_instance_schema_uri = 9;

    Returns
    Type
    Description
    string

    setPredictInstanceSchemaUri

    YAML schema file uri describing the format of a single instance, which are given to format this Endpoint's prediction (and explanation).

    If not set, we will generate predict schema from collected predict requests.

    Generated from protobuf field string predict_instance_schema_uri = 9;

    Parameter
    Name
    Description
    var
    string
    Returns
    Type
    Description
    $this

    getSamplePredictInstance

    Sample Predict instance, same format as PredictRequest.instances , this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri . If not set, we will generate predict schema from collected predict requests.

    Generated from protobuf field .google.protobuf.Value sample_predict_instance = 19;

    Returns
    Type
    Description
    Google\Protobuf\Value|null

    hasSamplePredictInstance

    clearSamplePredictInstance

    setSamplePredictInstance

    Sample Predict instance, same format as PredictRequest.instances , this can be set as a replacement of ModelDeploymentMonitoringJob.predict_instance_schema_uri . If not set, we will generate predict schema from collected predict requests.

    Generated from protobuf field .google.protobuf.Value sample_predict_instance = 19;

    Parameter
    Name
    Description
    var
    Google\Protobuf\Value
    Returns
    Type
    Description
    $this

    getAnalysisInstanceSchemaUri

    YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze.

    If this field is empty, all the feature data types are inferred from predict_instance_schema_uri , meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.

    Generated from protobuf field string analysis_instance_schema_uri = 16;

    Returns
    Type
    Description
    string

    setAnalysisInstanceSchemaUri

    YAML schema file uri describing the format of a single instance that you want Tensorflow Data Validation (TFDV) to analyze.

    If this field is empty, all the feature data types are inferred from predict_instance_schema_uri , meaning that TFDV will use the data in the exact format(data type) as prediction request/response. If there are any data type differences between predict instance and TFDV instance, this field can be used to override the schema. For models trained with Vertex AI, this field must be set as all the fields in predict instance formatted as string.

    Generated from protobuf field string analysis_instance_schema_uri = 16;

    Parameter
    Name
    Description
    var
    string
    Returns
    Type
    Description
    $this

    getBigqueryTables

    Output only. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum:

    1. Training data logging predict request/response
    2. Serving data logging predict request/response

    Generated from protobuf field repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    Google\Protobuf\Internal\RepeatedField

    setBigqueryTables

    Output only. The created bigquery tables for the job under customer project. Customer could do their own query & analysis. There could be 4 log tables in maximum:

    1. Training data logging predict request/response
    2. Serving data logging predict request/response

    Generated from protobuf field repeated .google.cloud.aiplatform.v1.ModelDeploymentMonitoringBigQueryTable bigquery_tables = 10 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    array< Google\Cloud\AIPlatform\V1\ModelDeploymentMonitoringBigQueryTable >
    Returns
    Type
    Description
    $this

    getLogTtl

    The TTL of BigQuery tables in user projects which stores logs.

    A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.

    Generated from protobuf field .google.protobuf.Duration log_ttl = 17;

    Returns
    Type
    Description
    Google\Protobuf\Duration|null

    hasLogTtl

    clearLogTtl

    setLogTtl

    The TTL of BigQuery tables in user projects which stores logs.

    A day is the basic unit of the TTL and we take the ceil of TTL/86400(a day). e.g. { second: 3600} indicates ttl = 1 day.

    Generated from protobuf field .google.protobuf.Duration log_ttl = 17;

    Parameter
    Name
    Description
    var
    Google\Protobuf\Duration
    Returns
    Type
    Description
    $this

    getLabels

    The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob.

    Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    Generated from protobuf field map<string, string> labels = 11;

    Returns
    Type
    Description
    Google\Protobuf\Internal\MapField

    setLabels

    The labels with user-defined metadata to organize your ModelDeploymentMonitoringJob.

    Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information and examples of labels.

    Generated from protobuf field map<string, string> labels = 11;

    Parameter
    Name
    Description
    var
    array|Google\Protobuf\Internal\MapField
    Returns
    Type
    Description
    $this

    getCreateTime

    Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

    Generated from protobuf field .google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    Google\Protobuf\Timestamp|null

    hasCreateTime

    clearCreateTime

    setCreateTime

    Output only. Timestamp when this ModelDeploymentMonitoringJob was created.

    Generated from protobuf field .google.protobuf.Timestamp create_time = 12 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    Google\Protobuf\Timestamp
    Returns
    Type
    Description
    $this

    getUpdateTime

    Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

    Generated from protobuf field .google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    Google\Protobuf\Timestamp|null

    hasUpdateTime

    clearUpdateTime

    setUpdateTime

    Output only. Timestamp when this ModelDeploymentMonitoringJob was updated most recently.

    Generated from protobuf field .google.protobuf.Timestamp update_time = 13 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    Google\Protobuf\Timestamp
    Returns
    Type
    Description
    $this

    getNextScheduleTime

    Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

    Generated from protobuf field .google.protobuf.Timestamp next_schedule_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    Google\Protobuf\Timestamp|null

    hasNextScheduleTime

    clearNextScheduleTime

    setNextScheduleTime

    Output only. Timestamp when this monitoring pipeline will be scheduled to run for the next round.

    Generated from protobuf field .google.protobuf.Timestamp next_schedule_time = 14 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    Google\Protobuf\Timestamp
    Returns
    Type
    Description
    $this

    getStatsAnomaliesBaseDirectory

    Stats anomalies base folder path.

    Generated from protobuf field .google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;

    Returns
    Type
    Description
    Google\Cloud\AIPlatform\V1\GcsDestination |null

    hasStatsAnomaliesBaseDirectory

    clearStatsAnomaliesBaseDirectory

    setStatsAnomaliesBaseDirectory

    Stats anomalies base folder path.

    Generated from protobuf field .google.cloud.aiplatform.v1.GcsDestination stats_anomalies_base_directory = 20;

    Parameter
    Name
    Description
    var
    Google\Cloud\AIPlatform\V1\GcsDestination
    Returns
    Type
    Description
    $this

    getEncryptionSpec

    Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.

    Generated from protobuf field .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 21;

    Returns
    Type
    Description
    Google\Cloud\AIPlatform\V1\EncryptionSpec |null

    hasEncryptionSpec

    clearEncryptionSpec

    setEncryptionSpec

    Customer-managed encryption key spec for a ModelDeploymentMonitoringJob. If set, this ModelDeploymentMonitoringJob and all sub-resources of this ModelDeploymentMonitoringJob will be secured by this key.

    Generated from protobuf field .google.cloud.aiplatform.v1.EncryptionSpec encryption_spec = 21;

    Parameter
    Name
    Description
    var
    Google\Cloud\AIPlatform\V1\EncryptionSpec
    Returns
    Type
    Description
    $this

    getEnableMonitoringPipelineLogs

    If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected.

    Please note the logs incur cost, which are subject to Cloud Logging pricing .

    Generated from protobuf field bool enable_monitoring_pipeline_logs = 22;

    Returns
    Type
    Description
    bool

    setEnableMonitoringPipelineLogs

    If true, the scheduled monitoring pipeline logs are sent to Google Cloud Logging, including pipeline status and anomalies detected.

    Please note the logs incur cost, which are subject to Cloud Logging pricing .

    Generated from protobuf field bool enable_monitoring_pipeline_logs = 22;

    Parameter
    Name
    Description
    var
    bool
    Returns
    Type
    Description
    $this

    getError

    Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED .

    Generated from protobuf field .google.rpc.Status error = 23 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Returns
    Type
    Description
    Google\Rpc\Status |null

    hasError

    clearError

    setError

    Output only. Only populated when the job's state is JOB_STATE_FAILED or JOB_STATE_CANCELLED .

    Generated from protobuf field .google.rpc.Status error = 23 [(.google.api.field_behavior) = OUTPUT_ONLY];

    Parameter
    Name
    Description
    var
    Google\Rpc\Status
    Returns
    Type
    Description
    $this
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