Uses of Class
onnx.OnnxMl.StringStringEntryProto

Packages that use OnnxMl.StringStringEntryProto

Package	Description
onnx

Uses of OnnxMl.StringStringEntryProto in onnx

Methods in onnx that return OnnxMl.StringStringEntryProto

Modifier and Type	Method	Description
OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.Builder.build()
OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.Builder.buildPartial()
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.getDefaultInstance()
OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.Builder.getDefaultInstanceForType()
OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.getDefaultInstanceForType()
OnnxMl.StringStringEntryProto	OnnxMl.TensorProto.Builder.getExternalData(int index)	Data can be stored inside the protobuf file using type-specific fields or raw_data.
OnnxMl.StringStringEntryProto	OnnxMl.TensorProto.getExternalData(int index)	Data can be stored inside the protobuf file using type-specific fields or raw_data.
OnnxMl.StringStringEntryProto	OnnxMl.TensorProtoOrBuilder.getExternalData(int index)	Data can be stored inside the protobuf file using type-specific fields or raw_data.
OnnxMl.StringStringEntryProto	OnnxMl.TrainingInfoProto.Builder.getInitializationBinding(int index)	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
OnnxMl.StringStringEntryProto	OnnxMl.TrainingInfoProto.getInitializationBinding(int index)	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
OnnxMl.StringStringEntryProto	OnnxMl.TrainingInfoProtoOrBuilder.getInitializationBinding(int index)	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
OnnxMl.StringStringEntryProto	OnnxMl.ModelProto.Builder.getMetadataProps(int index)	Named metadata values; keys should be distinct.
OnnxMl.StringStringEntryProto	OnnxMl.ModelProto.getMetadataProps(int index)	Named metadata values; keys should be distinct.
OnnxMl.StringStringEntryProto	OnnxMl.ModelProtoOrBuilder.getMetadataProps(int index)	Named metadata values; keys should be distinct.
OnnxMl.StringStringEntryProto	OnnxMl.TensorAnnotation.Builder.getQuantParameterTensorNames(int index)	<key, value> pairs to annotate tensor specified by <tensor_name> above.
OnnxMl.StringStringEntryProto	OnnxMl.TensorAnnotation.getQuantParameterTensorNames(int index)	<key, value> pairs to annotate tensor specified by <tensor_name> above.
OnnxMl.StringStringEntryProto	OnnxMl.TensorAnnotationOrBuilder.getQuantParameterTensorNames(int index)	<key, value> pairs to annotate tensor specified by <tensor_name> above.
OnnxMl.StringStringEntryProto	OnnxMl.TrainingInfoProto.Builder.getUpdateBinding(int index)	Gradient-based training is usually an iterative procedure.
OnnxMl.StringStringEntryProto	OnnxMl.TrainingInfoProto.getUpdateBinding(int index)	Gradient-based training is usually an iterative procedure.
OnnxMl.StringStringEntryProto	OnnxMl.TrainingInfoProtoOrBuilder.getUpdateBinding(int index)	Gradient-based training is usually an iterative procedure.
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseDelimitedFrom(InputStream input)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseDelimitedFrom(InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(byte[] data)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(byte[] data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(InputStream input)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(ByteBuffer data)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(ByteBuffer data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(org.nd4j.shade.protobuf.ByteString data)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(org.nd4j.shade.protobuf.ByteString data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(org.nd4j.shade.protobuf.CodedInputStream input)
static OnnxMl.StringStringEntryProto	OnnxMl.StringStringEntryProto.parseFrom(org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry)

Methods in onnx that return types with arguments of type OnnxMl.StringStringEntryProto

Modifier and Type	Method	Description
List<OnnxMl.StringStringEntryProto>	OnnxMl.TensorProto.Builder.getExternalDataList()	Data can be stored inside the protobuf file using type-specific fields or raw_data.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TensorProto.getExternalDataList()	Data can be stored inside the protobuf file using type-specific fields or raw_data.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TensorProtoOrBuilder.getExternalDataList()	Data can be stored inside the protobuf file using type-specific fields or raw_data.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TrainingInfoProto.Builder.getInitializationBindingList()	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TrainingInfoProto.getInitializationBindingList()	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TrainingInfoProtoOrBuilder.getInitializationBindingList()	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
List<OnnxMl.StringStringEntryProto>	OnnxMl.ModelProto.Builder.getMetadataPropsList()	Named metadata values; keys should be distinct.
List<OnnxMl.StringStringEntryProto>	OnnxMl.ModelProto.getMetadataPropsList()	Named metadata values; keys should be distinct.
List<OnnxMl.StringStringEntryProto>	OnnxMl.ModelProtoOrBuilder.getMetadataPropsList()	Named metadata values; keys should be distinct.
org.nd4j.shade.protobuf.Parser<OnnxMl.StringStringEntryProto>	OnnxMl.StringStringEntryProto.getParserForType()
List<OnnxMl.StringStringEntryProto>	OnnxMl.TensorAnnotation.Builder.getQuantParameterTensorNamesList()	<key, value> pairs to annotate tensor specified by <tensor_name> above.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TensorAnnotation.getQuantParameterTensorNamesList()	<key, value> pairs to annotate tensor specified by <tensor_name> above.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TensorAnnotationOrBuilder.getQuantParameterTensorNamesList()	<key, value> pairs to annotate tensor specified by <tensor_name> above.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TrainingInfoProto.Builder.getUpdateBindingList()	Gradient-based training is usually an iterative procedure.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TrainingInfoProto.getUpdateBindingList()	Gradient-based training is usually an iterative procedure.
List<OnnxMl.StringStringEntryProto>	OnnxMl.TrainingInfoProtoOrBuilder.getUpdateBindingList()	Gradient-based training is usually an iterative procedure.
static org.nd4j.shade.protobuf.Parser<OnnxMl.StringStringEntryProto>	OnnxMl.StringStringEntryProto.parser()

Methods in onnx with parameters of type OnnxMl.StringStringEntryProto

Modifier and Type	Method	Description
OnnxMl.TensorProto.Builder	OnnxMl.TensorProto.Builder.addExternalData(int index, OnnxMl.StringStringEntryProto value)	Data can be stored inside the protobuf file using type-specific fields or raw_data.
OnnxMl.TensorProto.Builder	OnnxMl.TensorProto.Builder.addExternalData(OnnxMl.StringStringEntryProto value)	Data can be stored inside the protobuf file using type-specific fields or raw_data.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.addInitializationBinding(int index, OnnxMl.StringStringEntryProto value)	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.addInitializationBinding(OnnxMl.StringStringEntryProto value)	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
OnnxMl.ModelProto.Builder	OnnxMl.ModelProto.Builder.addMetadataProps(int index, OnnxMl.StringStringEntryProto value)	Named metadata values; keys should be distinct.
OnnxMl.ModelProto.Builder	OnnxMl.ModelProto.Builder.addMetadataProps(OnnxMl.StringStringEntryProto value)	Named metadata values; keys should be distinct.
OnnxMl.TensorAnnotation.Builder	OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNames(int index, OnnxMl.StringStringEntryProto value)	<key, value> pairs to annotate tensor specified by <tensor_name> above.
OnnxMl.TensorAnnotation.Builder	OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNames(OnnxMl.StringStringEntryProto value)	<key, value> pairs to annotate tensor specified by <tensor_name> above.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.addUpdateBinding(int index, OnnxMl.StringStringEntryProto value)	Gradient-based training is usually an iterative procedure.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.addUpdateBinding(OnnxMl.StringStringEntryProto value)	Gradient-based training is usually an iterative procedure.
OnnxMl.StringStringEntryProto.Builder	OnnxMl.StringStringEntryProto.Builder.mergeFrom(OnnxMl.StringStringEntryProto other)
static OnnxMl.StringStringEntryProto.Builder	OnnxMl.StringStringEntryProto.newBuilder(OnnxMl.StringStringEntryProto prototype)
OnnxMl.TensorProto.Builder	OnnxMl.TensorProto.Builder.setExternalData(int index, OnnxMl.StringStringEntryProto value)	Data can be stored inside the protobuf file using type-specific fields or raw_data.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.setInitializationBinding(int index, OnnxMl.StringStringEntryProto value)	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
OnnxMl.ModelProto.Builder	OnnxMl.ModelProto.Builder.setMetadataProps(int index, OnnxMl.StringStringEntryProto value)	Named metadata values; keys should be distinct.
OnnxMl.TensorAnnotation.Builder	OnnxMl.TensorAnnotation.Builder.setQuantParameterTensorNames(int index, OnnxMl.StringStringEntryProto value)	<key, value> pairs to annotate tensor specified by <tensor_name> above.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.setUpdateBinding(int index, OnnxMl.StringStringEntryProto value)	Gradient-based training is usually an iterative procedure.

Method parameters in onnx with type arguments of type OnnxMl.StringStringEntryProto

Modifier and Type	Method	Description
OnnxMl.TensorProto.Builder	OnnxMl.TensorProto.Builder.addAllExternalData(Iterable<? extends OnnxMl.StringStringEntryProto> values)	Data can be stored inside the protobuf file using type-specific fields or raw_data.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.addAllInitializationBinding(Iterable<? extends OnnxMl.StringStringEntryProto> values)	This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto.
OnnxMl.ModelProto.Builder	OnnxMl.ModelProto.Builder.addAllMetadataProps(Iterable<? extends OnnxMl.StringStringEntryProto> values)	Named metadata values; keys should be distinct.
OnnxMl.TensorAnnotation.Builder	OnnxMl.TensorAnnotation.Builder.addAllQuantParameterTensorNames(Iterable<? extends OnnxMl.StringStringEntryProto> values)	<key, value> pairs to annotate tensor specified by <tensor_name> above.
OnnxMl.TrainingInfoProto.Builder	OnnxMl.TrainingInfoProto.Builder.addAllUpdateBinding(Iterable<? extends OnnxMl.StringStringEntryProto> values)	Gradient-based training is usually an iterative procedure.