Schema

final class Schema extends GeneratedMessageV3 with SchemaOrBuilder


Message to represent schema information.
NextID: 15

Protobuf type tensorflow.metadata.v0.Schema

Source: Schema.java

Linear Supertypes

SchemaOrBuilder, GeneratedMessageV3, Serializable, AbstractMessage, Message, MessageOrBuilder, AbstractMessageLite[MessageType, BuilderType], MessageLite, MessageLiteOrBuilder, AnyRef, Any

Ordering

Alphabetic
By Inheritance

Inherited

Schema
SchemaOrBuilder
GeneratedMessageV3
Serializable
AbstractMessage
Message
MessageOrBuilder
AbstractMessageLite
MessageLite
MessageLiteOrBuilder
AnyRef
Any

Hide All
Show All

Visibility

Public
Protected

Value Members

final def !=(arg0: Any): Boolean
Definition Classes
AnyRef → Any
final def ##: Int
Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean
Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0
Definition Classes
Any
def clone(): AnyRef
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.CloneNotSupportedException]) @native()

def containsTensorRepresentationGroup(key: String): Boolean

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

final def eq(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def equals(obj: AnyRef): Boolean
Definition Classes
Schema → AbstractMessage → Message → AnyRef → Any
Annotations
@Override()
def finalize(): Unit
Attributes
protected[lang]
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.Throwable])
def findInitializationErrors(): List[String]
Definition Classes
AbstractMessage → MessageOrBuilder
def getAllFields(): Map[FieldDescriptor, AnyRef]
Definition Classes
GeneratedMessageV3 → MessageOrBuilder

def getAnnotation(): Annotation

Additional information about the schema as a whole. Features may also
be annotated individually.

Additional information about the schema as a whole. Features may also
be annotated individually.

optional .tensorflow.metadata.v0.Annotation annotation = 8;

returns: The annotation.

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getAnnotationOrBuilder(): AnnotationOrBuilder

Additional information about the schema as a whole. Features may also
be annotated individually.

Additional information about the schema as a whole. Features may also
be annotated individually.

optional .tensorflow.metadata.v0.Annotation annotation = 8;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

final def getClass(): Class[_ <: AnyRef]
Definition Classes
AnyRef → Any
Annotations
@native()
def getDatasetConstraints(): DatasetConstraints
```
Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.
```
```
Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.
```
optional .tensorflow.metadata.v0.DatasetConstraints dataset_constraints = 11;
returns
The datasetConstraints.
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()

def getDatasetConstraintsOrBuilder(): DatasetConstraintsOrBuilder

Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.

Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.

optional .tensorflow.metadata.v0.DatasetConstraints dataset_constraints = 11;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getDefaultEnvironment(index: Int): String

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

repeated string default_environment = 5;

index: The index of the element to return.
returns: The defaultEnvironment at the given index.

Definition Classes: Schema → SchemaOrBuilder

def getDefaultEnvironmentBytes(index: Int): ByteString

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

repeated string default_environment = 5;

index: The index of the value to return.
returns: The bytes of the defaultEnvironment at the given index.

Definition Classes: Schema → SchemaOrBuilder

def getDefaultEnvironmentCount(): Int

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

repeated string default_environment = 5;

returns: The count of defaultEnvironment.

Definition Classes: Schema → SchemaOrBuilder

def getDefaultEnvironmentList(): ProtocolStringList

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

Default environments for each feature.
An environment represents both a type of location (e.g. a server or phone)
and a time (e.g. right before model X is run). In the standard scenario,
99% of the features should be in the default environments TRAINING,
SERVING, and the LABEL (or labels) AND WEIGHT is only available at TRAINING
(not at serving).
Other possible variations:
1. There may be TRAINING_MOBILE, SERVING_MOBILE, TRAINING_SERVICE,
   and SERVING_SERVICE.
2. If one is ensembling three models, where the predictions of the first
   three models are available for the ensemble model, there may be
   TRAINING, SERVING_INITIAL, SERVING_ENSEMBLE.
See FeatureProto::not_in_environment and FeatureProto::in_environment.

repeated string default_environment = 5;

returns: A list containing the defaultEnvironment.

Definition Classes: Schema → SchemaOrBuilder

def getDefaultInstanceForType(): Schema
Definition Classes
Schema → MessageOrBuilder → MessageLiteOrBuilder
Annotations
@Override()
def getDescriptorForType(): Descriptor
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getFeature(index: Int): Feature
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFeatureCount(): Int
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFeatureList(): List[Feature]
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFeatureOrBuilder(index: Int): FeatureOrBuilder
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFeatureOrBuilderList(): List[_ <: FeatureOrBuilder]
```
Features described in this schema.
```
```
Features described in this schema.
```
repeated .tensorflow.metadata.v0.Feature feature = 1;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getField(field: FieldDescriptor): AnyRef
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getFloatDomain(index: Int): FloatDomain
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFloatDomainCount(): Int
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFloatDomainList(): List[FloatDomain]
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFloatDomainOrBuilder(index: Int): FloatDomainOrBuilder
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getFloatDomainOrBuilderList(): List[_ <: FloatDomainOrBuilder]
```
top level float domains that can be reused by features
```
```
top level float domains that can be reused by features
```
repeated .tensorflow.metadata.v0.FloatDomain float_domain = 9;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getInitializationErrorString(): String
Definition Classes
AbstractMessage → MessageOrBuilder
def getIntDomain(index: Int): IntDomain
```
top level int domains that can be reused by features
```
```
top level int domains that can be reused by features
```
repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getIntDomainCount(): Int
```
top level int domains that can be reused by features
```
```
top level int domains that can be reused by features
```
repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getIntDomainList(): List[IntDomain]
```
top level int domains that can be reused by features
```
```
top level int domains that can be reused by features
```
repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getIntDomainOrBuilder(index: Int): IntDomainOrBuilder
```
top level int domains that can be reused by features
```
```
top level int domains that can be reused by features
```
repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getIntDomainOrBuilderList(): List[_ <: IntDomainOrBuilder]
```
top level int domains that can be reused by features
```
```
top level int domains that can be reused by features
```
repeated .tensorflow.metadata.v0.IntDomain int_domain = 10;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getOneofFieldDescriptor(oneof: OneofDescriptor): FieldDescriptor
Definition Classes
GeneratedMessageV3 → AbstractMessage → MessageOrBuilder
def getParserForType(): Parser[Schema]
Definition Classes
Schema → GeneratedMessageV3 → Message → MessageLite
Annotations
@Override()
def getRepeatedField(field: FieldDescriptor, index: Int): AnyRef
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getRepeatedFieldCount(field: FieldDescriptor): Int
Definition Classes
GeneratedMessageV3 → MessageOrBuilder

def getRepresentVariableLengthAsRagged(): Boolean

Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.

Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.

optional bool represent_variable_length_as_ragged = 14;

returns: The representVariableLengthAsRagged.

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getSerializedSize(): Int
Definition Classes
Schema → GeneratedMessageV3 → AbstractMessage → MessageLite
Annotations
@Override()
def getSparseFeature(index: Int): SparseFeature
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getSparseFeatureCount(): Int
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getSparseFeatureList(): List[SparseFeature]
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getSparseFeatureOrBuilder(index: Int): SparseFeatureOrBuilder
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getSparseFeatureOrBuilderList(): List[_ <: SparseFeatureOrBuilder]
```
Sparse features described in this schema.
```
```
Sparse features described in this schema.
```
repeated .tensorflow.metadata.v0.SparseFeature sparse_feature = 6;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()

def getStringDomain(index: Int): StringDomain

declared as top-level features in <feature>.
String domains referenced in the features.

declared as top-level features in <feature>.
String domains referenced in the features.

repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getStringDomainCount(): Int

declared as top-level features in <feature>.
String domains referenced in the features.

declared as top-level features in <feature>.
String domains referenced in the features.

repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getStringDomainList(): List[StringDomain]

declared as top-level features in <feature>.
String domains referenced in the features.

declared as top-level features in <feature>.
String domains referenced in the features.

repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getStringDomainOrBuilder(index: Int): StringDomainOrBuilder
```
declared as top-level features in <feature>.
String domains referenced in the features.
```
```
declared as top-level features in <feature>.
String domains referenced in the features.
```
repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getStringDomainOrBuilderList(): List[_ <: StringDomainOrBuilder]
```
declared as top-level features in <feature>.
String domains referenced in the features.
```
```
declared as top-level features in <feature>.
String domains referenced in the features.
```
repeated .tensorflow.metadata.v0.StringDomain string_domain = 4;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()

def getTensorRepresentationGroupCount(): Int

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

Definition Classes: Schema → SchemaOrBuilder

def getTensorRepresentationGroupMap(): Map[String, TensorRepresentationGroup]

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getTensorRepresentationGroupOrDefault(key: String, defaultValue: TensorRepresentationGroup): TensorRepresentationGroup

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getTensorRepresentationGroupOrThrow(key: String): TensorRepresentationGroup

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

TensorRepresentation groups. The keys are the names of the groups.
Key "" (empty string) denotes the "default" group, which is what should
be used when a group name is not provided.
See the documentation at TensorRepresentationGroup for more info.
Under development.

map<string, .tensorflow.metadata.v0.TensorRepresentationGroup> tensor_representation_group = 13;

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def getUnknownFields(): UnknownFieldSet
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def getWeightedFeature(index: Int): WeightedFeature
```
Weighted features described in this schema.
```
```
Weighted features described in this schema.
```
repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getWeightedFeatureCount(): Int
```
Weighted features described in this schema.
```
```
Weighted features described in this schema.
```
repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getWeightedFeatureList(): List[WeightedFeature]
```
Weighted features described in this schema.
```
```
Weighted features described in this schema.
```
repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getWeightedFeatureOrBuilder(index: Int): WeightedFeatureOrBuilder
```
Weighted features described in this schema.
```
```
Weighted features described in this schema.
```
repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def getWeightedFeatureOrBuilderList(): List[_ <: WeightedFeatureOrBuilder]
```
Weighted features described in this schema.
```
```
Weighted features described in this schema.
```
repeated .tensorflow.metadata.v0.WeightedFeature weighted_feature = 12;
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()

def hasAnnotation(): Boolean

Additional information about the schema as a whole. Features may also
be annotated individually.

Additional information about the schema as a whole. Features may also
be annotated individually.

optional .tensorflow.metadata.v0.Annotation annotation = 8;

returns: Whether the annotation field is set.

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def hasDatasetConstraints(): Boolean
```
Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.
```
```
Dataset-level constraints. This is currently used for specifying
information about changes in num_examples.
```
optional .tensorflow.metadata.v0.DatasetConstraints dataset_constraints = 11;
returns
Whether the datasetConstraints field is set.
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override()
def hasField(field: FieldDescriptor): Boolean
Definition Classes
GeneratedMessageV3 → MessageOrBuilder
def hasOneof(oneof: OneofDescriptor): Boolean
Definition Classes
GeneratedMessageV3 → AbstractMessage → MessageOrBuilder

def hasRepresentVariableLengthAsRagged(): Boolean

Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.

Whether to represent variable length features as RaggedTensors. By default
they are represented as ragged left-alighned SparseTensors. RaggedTensor
representation is more memory efficient. Therefore, turning this on will
likely yield data processing performance improvement.
Experimental and may be subject to change.

optional bool represent_variable_length_as_ragged = 14;

returns: Whether the representVariableLengthAsRagged field is set.

Definition Classes: Schema → SchemaOrBuilder
Annotations: @Override()

def hashCode(): Int
Definition Classes
Schema → AbstractMessage → Message → AnyRef → Any
Annotations
@Override()
def internalGetFieldAccessorTable(): FieldAccessorTable
Attributes
protected[v0]
Definition Classes
Schema → GeneratedMessageV3
Annotations
@Override()
def internalGetMapFieldReflection(number: Int): MapFieldReflectionAccessor
Attributes
protected[v0]
Definition Classes
Schema → GeneratedMessageV3
Annotations
@SuppressWarnings() @Override()
final def isInitialized(): Boolean
Definition Classes
Schema → GeneratedMessageV3 → AbstractMessage → MessageLiteOrBuilder
Annotations
@Override()
final def isInstanceOf[T0]: Boolean
Definition Classes
Any
def makeExtensionsImmutable(): Unit
Attributes
protected[protobuf]
Definition Classes
GeneratedMessageV3
final def ne(arg0: AnyRef): Boolean
Definition Classes
AnyRef
def newBuilderForType(parent: BuilderParent): Builder
Attributes
protected[v0]
Definition Classes
Schema → GeneratedMessageV3
Annotations
@Override()
def newBuilderForType(): Builder
Definition Classes
Schema → Message → MessageLite
Annotations
@Override()
def newBuilderForType(parent: BuilderParent): Builder
Attributes
protected[protobuf]
Definition Classes
GeneratedMessageV3 → AbstractMessage
def newInstance(unused: UnusedPrivateParameter): AnyRef
Attributes
protected[v0]
Definition Classes
Schema → GeneratedMessageV3
Annotations
@Override() @SuppressWarnings()
final def notify(): Unit
Definition Classes
AnyRef
Annotations
@native()
final def notifyAll(): Unit
Definition Classes
AnyRef
Annotations
@native()
def parseUnknownField(input: CodedInputStream, unknownFields: Builder, extensionRegistry: ExtensionRegistryLite, tag: Int): Boolean
Attributes
protected[protobuf]
Definition Classes
GeneratedMessageV3
Annotations
@throws(classOf[java.io.IOException])
def parseUnknownFieldProto3(input: CodedInputStream, unknownFields: Builder, extensionRegistry: ExtensionRegistryLite, tag: Int): Boolean
Attributes
protected[protobuf]
Definition Classes
GeneratedMessageV3
Annotations
@throws(classOf[java.io.IOException])
final def synchronized[T0](arg0: => T0): T0
Definition Classes
AnyRef
def toBuilder(): Builder
Definition Classes
Schema → Message → MessageLite
Annotations
@Override()
def toByteArray(): Array[Byte]
Definition Classes
AbstractMessageLite → MessageLite
def toByteString(): ByteString
Definition Classes
AbstractMessageLite → MessageLite
final def toString(): String
Definition Classes
AbstractMessage → Message → AnyRef → Any
final def wait(): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long, arg1: Int): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException])
final def wait(arg0: Long): Unit
Definition Classes
AnyRef
Annotations
@throws(classOf[java.lang.InterruptedException]) @native()
def writeDelimitedTo(output: OutputStream): Unit
Definition Classes
AbstractMessageLite → MessageLite
Annotations
@throws(classOf[java.io.IOException])
def writeReplace(): AnyRef
Attributes
protected[protobuf]
Definition Classes
GeneratedMessageV3
Annotations
@throws(classOf[java.io.ObjectStreamException])
def writeTo(output: CodedOutputStream): Unit
Definition Classes
Schema → GeneratedMessageV3 → AbstractMessage → MessageLite
Annotations
@Override()
def writeTo(output: OutputStream): Unit
Definition Classes
AbstractMessageLite → MessageLite
Annotations
@throws(classOf[java.io.IOException])

Deprecated Value Members

def getTensorRepresentationGroup(): Map[String, TensorRepresentationGroup]
Use #getTensorRepresentationGroupMap() instead.
Use #getTensorRepresentationGroupMap() instead.
Definition Classes
Schema → SchemaOrBuilder
Annotations
@Override() @Deprecated
Deprecated
def internalGetMapField(fieldNumber: Int): MapField[_ <: AnyRef, _ <: AnyRef]
Attributes
protected[protobuf]
Definition Classes
GeneratedMessageV3
Annotations
@Deprecated
Deprecated
def mergeFromAndMakeImmutableInternal(input: CodedInputStream, extensionRegistry: ExtensionRegistryLite): Unit
Attributes
protected[protobuf]
Definition Classes
GeneratedMessageV3
Annotations
@throws(classOf[com.google.protobuf.InvalidProtocolBufferException]) @Deprecated
Deprecated

Packages

Schema

final class Schema extends GeneratedMessageV3 with SchemaOrBuilder

Value Members

Deprecated Value Members

Inherited from SchemaOrBuilder

Inherited from GeneratedMessageV3

Inherited from Serializable

Inherited from AbstractMessage

Inherited from Message

Inherited from MessageOrBuilder

Inherited from AbstractMessageLite[MessageType, BuilderType]

Inherited from MessageLite

Inherited from MessageLiteOrBuilder

Inherited from AnyRef

Inherited from Any

Ungrouped

Packages

Schema

final class Schema extends GeneratedMessageV3 with SchemaOrBuilder

Value Members

Deprecated Value Members

Inherited from SchemaOrBuilder

Inherited from GeneratedMessageV3

Inherited from Serializable

Inherited from AbstractMessage

Inherited from Message

Inherited from MessageOrBuilder

Inherited from AbstractMessageLite[MessageType, BuilderType]

Inherited from MessageLite

Inherited from MessageLiteOrBuilder

Inherited from AnyRef

Inherited from Any

Ungrouped

Schema