Uses of Class
ai.onnx.proto.OnnxMl.StringStringEntryProto.Builder
Packages that use OnnxMl.StringStringEntryProto.Builder
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Uses of OnnxMl.StringStringEntryProto.Builder in ai.onnx.proto
Methods in ai.onnx.proto that return OnnxMl.StringStringEntryProto.BuilderModifier and TypeMethodDescriptionOnnxMl.TensorProto.Builder.addExternalDataBuilder()Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TensorProto.Builder.addExternalDataBuilder(int index) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.addInitializationBindingBuilder()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.addInitializationBindingBuilder(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.ModelProto.Builder.addMetadataPropsBuilder()Named metadata values; keys should be distinct.OnnxMl.ModelProto.Builder.addMetadataPropsBuilder(int index) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNamesBuilder()<key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNamesBuilder(int index) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.StringStringEntryProto.Builder.addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value) OnnxMl.TrainingInfoProto.Builder.addUpdateBindingBuilder()Gradient-based training is usually an iterative procedure.OnnxMl.TrainingInfoProto.Builder.addUpdateBindingBuilder(int index) Gradient-based training is usually an iterative procedure.OnnxMl.StringStringEntryProto.Builder.clear()OnnxMl.StringStringEntryProto.Builder.clearField(com.google.protobuf.Descriptors.FieldDescriptor field) OnnxMl.StringStringEntryProto.Builder.clearKey()optional string key = 1;OnnxMl.StringStringEntryProto.Builder.clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof) OnnxMl.StringStringEntryProto.Builder.clearValue()optional string value = 2;OnnxMl.StringStringEntryProto.Builder.clone()OnnxMl.TensorProto.Builder.getExternalDataBuilder(int index) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.getInitializationBindingBuilder(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.ModelProto.Builder.getMetadataPropsBuilder(int index) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.getQuantParameterTensorNamesBuilder(int index) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.getUpdateBindingBuilder(int index) Gradient-based training is usually an iterative procedure.OnnxMl.StringStringEntryProto.Builder.mergeFrom(OnnxMl.StringStringEntryProto other) OnnxMl.StringStringEntryProto.Builder.mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) OnnxMl.StringStringEntryProto.Builder.mergeFrom(com.google.protobuf.Message other) OnnxMl.StringStringEntryProto.Builder.mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields) OnnxMl.StringStringEntryProto.newBuilder()OnnxMl.StringStringEntryProto.newBuilder(OnnxMl.StringStringEntryProto prototype) OnnxMl.StringStringEntryProto.newBuilderForType()protected OnnxMl.StringStringEntryProto.BuilderOnnxMl.StringStringEntryProto.newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) OnnxMl.StringStringEntryProto.Builder.setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value) optional string key = 1;OnnxMl.StringStringEntryProto.Builder.setKeyBytes(com.google.protobuf.ByteString value) optional string key = 1;OnnxMl.StringStringEntryProto.Builder.setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value) OnnxMl.StringStringEntryProto.Builder.setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields) optional string value = 2;OnnxMl.StringStringEntryProto.Builder.setValueBytes(com.google.protobuf.ByteString value) optional string value = 2;OnnxMl.StringStringEntryProto.toBuilder()Methods in ai.onnx.proto that return types with arguments of type OnnxMl.StringStringEntryProto.BuilderModifier and TypeMethodDescriptionOnnxMl.TensorProto.Builder.getExternalDataBuilderList()Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.getInitializationBindingBuilderList()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.getMetadataPropsBuilderList()Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.getQuantParameterTensorNamesBuilderList()<key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.getUpdateBindingBuilderList()Gradient-based training is usually an iterative procedure.Methods in ai.onnx.proto with parameters of type OnnxMl.StringStringEntryProto.BuilderModifier and TypeMethodDescriptionOnnxMl.TensorProto.Builder.addExternalData(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TensorProto.Builder.addExternalData(OnnxMl.StringStringEntryProto.Builder builderForValue) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.addInitializationBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) 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.addInitializationBinding(OnnxMl.StringStringEntryProto.Builder builderForValue) 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.addMetadataProps(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) Named metadata values; keys should be distinct.OnnxMl.ModelProto.Builder.addMetadataProps(OnnxMl.StringStringEntryProto.Builder builderForValue) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNames(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TensorAnnotation.Builder.addQuantParameterTensorNames(OnnxMl.StringStringEntryProto.Builder builderForValue) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.addUpdateBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) Gradient-based training is usually an iterative procedure.OnnxMl.TrainingInfoProto.Builder.addUpdateBinding(OnnxMl.StringStringEntryProto.Builder builderForValue) Gradient-based training is usually an iterative procedure.OnnxMl.TensorProto.Builder.setExternalData(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) Data can be stored inside the protobuf file using type-specific fields or raw_data.OnnxMl.TrainingInfoProto.Builder.setInitializationBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) 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.setMetadataProps(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) Named metadata values; keys should be distinct.OnnxMl.TensorAnnotation.Builder.setQuantParameterTensorNames(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) <key, value> pairs to annotate tensor specified by <tensor_name> above.OnnxMl.TrainingInfoProto.Builder.setUpdateBinding(int index, OnnxMl.StringStringEntryProto.Builder builderForValue) Gradient-based training is usually an iterative procedure.