Package ai.onnx.proto

Class OnnxMl.ModelProto.Builder

java.lang.Object
com.google.protobuf.AbstractMessageLite.Builder
com.google.protobuf.AbstractMessage.Builder<BuilderType>
com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>
ai.onnx.proto.OnnxMl.ModelProto.Builder
All Implemented Interfaces:
OnnxMl.ModelProtoOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Cloneable
Enclosing class:
OnnxMl.ModelProto
public static final class OnnxMl.ModelProto.Builder extends com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder> implements OnnxMl.ModelProtoOrBuilder
 Models
 ModelProto is a top-level file/container format for bundling a ML model and
 associating its computation graph with metadata.
 The semantics of the model are described by the associated GraphProto's.
Protobuf type onnx.ModelProto

  • Method Details

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • clear

      public OnnxMl.ModelProto.Builder clear()
      Specified by:
      clear in interface com.google.protobuf.Message.Builder
      Specified by:
      clear in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clear in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • getDescriptorForType

      public com.google.protobuf.Descriptors.Descriptor getDescriptorForType()
      Specified by:
      getDescriptorForType in interface com.google.protobuf.Message.Builder
      Specified by:
      getDescriptorForType in interface com.google.protobuf.MessageOrBuilder
      Overrides:
      getDescriptorForType in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • getDefaultInstanceForType

      public OnnxMl.ModelProto getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

    • build

      public OnnxMl.ModelProto build()
      Specified by:
      build in interface com.google.protobuf.Message.Builder
      Specified by:
      build in interface com.google.protobuf.MessageLite.Builder

    • buildPartial

      public OnnxMl.ModelProto buildPartial()
      Specified by:
      buildPartial in interface com.google.protobuf.Message.Builder
      Specified by:
      buildPartial in interface com.google.protobuf.MessageLite.Builder

    • clone

      public OnnxMl.ModelProto.Builder clone()
      Specified by:
      clone in interface com.google.protobuf.Message.Builder
      Specified by:
      clone in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      clone in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • setField

      public OnnxMl.ModelProto.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
      Specified by:
      setField in interface com.google.protobuf.Message.Builder
      Overrides:
      setField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • clearField

      public OnnxMl.ModelProto.Builder clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
      Specified by:
      clearField in interface com.google.protobuf.Message.Builder
      Overrides:
      clearField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • clearOneof

      public OnnxMl.ModelProto.Builder clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
      Specified by:
      clearOneof in interface com.google.protobuf.Message.Builder
      Overrides:
      clearOneof in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • setRepeatedField

      public OnnxMl.ModelProto.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
      Specified by:
      setRepeatedField in interface com.google.protobuf.Message.Builder
      Overrides:
      setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • addRepeatedField

      public OnnxMl.ModelProto.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
      Specified by:
      addRepeatedField in interface com.google.protobuf.Message.Builder
      Overrides:
      addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • mergeFrom

      public OnnxMl.ModelProto.Builder mergeFrom(com.google.protobuf.Message other)
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<OnnxMl.ModelProto.Builder>

    • mergeFrom

      public OnnxMl.ModelProto.Builder mergeFrom(OnnxMl.ModelProto other)

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • mergeFrom

      public OnnxMl.ModelProto.Builder mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Specified by:
      mergeFrom in interface com.google.protobuf.Message.Builder
      Specified by:
      mergeFrom in interface com.google.protobuf.MessageLite.Builder
      Overrides:
      mergeFrom in class com.google.protobuf.AbstractMessage.Builder<OnnxMl.ModelProto.Builder>
      Throws:
      IOException

    • hasIrVersion

      public boolean hasIrVersion()
       The version of the IR this model targets. See Version enum above.
 This field MUST be present.
      optional int64 ir_version = 1;
      Specified by:
      hasIrVersion in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      Whether the irVersion field is set.

    • getIrVersion

      public long getIrVersion()
       The version of the IR this model targets. See Version enum above.
 This field MUST be present.
      optional int64 ir_version = 1;
      Specified by:
      getIrVersion in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The irVersion.

    • setIrVersion

      public OnnxMl.ModelProto.Builder setIrVersion(long value)
       The version of the IR this model targets. See Version enum above.
 This field MUST be present.
      optional int64 ir_version = 1;
      Parameters:
      value - The irVersion to set.
      Returns:
      This builder for chaining.

    • clearIrVersion

      public OnnxMl.ModelProto.Builder clearIrVersion()
       The version of the IR this model targets. See Version enum above.
 This field MUST be present.
      optional int64 ir_version = 1;
      Returns:
      This builder for chaining.

    • getOpsetImportList

      public List<OnnxMl.OperatorSetIdProto> getOpsetImportList()
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;
      Specified by:
      getOpsetImportList in interface OnnxMl.ModelProtoOrBuilder

    • getOpsetImportCount

      public int getOpsetImportCount()
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;
      Specified by:
      getOpsetImportCount in interface OnnxMl.ModelProtoOrBuilder

    • getOpsetImport

      public OnnxMl.OperatorSetIdProto getOpsetImport(int index)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;
      Specified by:
      getOpsetImport in interface OnnxMl.ModelProtoOrBuilder

    • setOpsetImport

      public OnnxMl.ModelProto.Builder setOpsetImport(int index, OnnxMl.OperatorSetIdProto value)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • setOpsetImport

      public OnnxMl.ModelProto.Builder setOpsetImport(int index, OnnxMl.OperatorSetIdProto.Builder builderForValue)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • addOpsetImport

      public OnnxMl.ModelProto.Builder addOpsetImport(OnnxMl.OperatorSetIdProto value)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • addOpsetImport

      public OnnxMl.ModelProto.Builder addOpsetImport(int index, OnnxMl.OperatorSetIdProto value)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • addOpsetImport

      public OnnxMl.ModelProto.Builder addOpsetImport(OnnxMl.OperatorSetIdProto.Builder builderForValue)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • addOpsetImport

      public OnnxMl.ModelProto.Builder addOpsetImport(int index, OnnxMl.OperatorSetIdProto.Builder builderForValue)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • addAllOpsetImport

      public OnnxMl.ModelProto.Builder addAllOpsetImport(Iterable<? extends OnnxMl.OperatorSetIdProto> values)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • clearOpsetImport

      public OnnxMl.ModelProto.Builder clearOpsetImport()
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • removeOpsetImport

      public OnnxMl.ModelProto.Builder removeOpsetImport(int index)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • getOpsetImportBuilder

      public OnnxMl.OperatorSetIdProto.Builder getOpsetImportBuilder(int index)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • getOpsetImportOrBuilder

      public OnnxMl.OperatorSetIdProtoOrBuilder getOpsetImportOrBuilder(int index)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;
      Specified by:
      getOpsetImportOrBuilder in interface OnnxMl.ModelProtoOrBuilder

    • getOpsetImportOrBuilderList

      public List<? extends OnnxMl.OperatorSetIdProtoOrBuilder> getOpsetImportOrBuilderList()
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;
      Specified by:
      getOpsetImportOrBuilderList in interface OnnxMl.ModelProtoOrBuilder

    • addOpsetImportBuilder

      public OnnxMl.OperatorSetIdProto.Builder addOpsetImportBuilder()
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • addOpsetImportBuilder

      public OnnxMl.OperatorSetIdProto.Builder addOpsetImportBuilder(int index)
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • getOpsetImportBuilderList

      public List<OnnxMl.OperatorSetIdProto.Builder> getOpsetImportBuilderList()
       The OperatorSets this model relies on.
 All ModelProtos MUST have at least one entry that
 specifies which version of the ONNX OperatorSet is
 being imported.
 All nodes in the ModelProto's graph will bind against the operator
 with the same-domain/same-op_type operator with the HIGHEST version
 in the referenced operator sets.
      repeated .onnx.OperatorSetIdProto opset_import = 8;

    • hasProducerName

      public boolean hasProducerName()
       The name of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_name = 2;
      Specified by:
      hasProducerName in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      Whether the producerName field is set.

    • getProducerName

      public String getProducerName()
       The name of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_name = 2;
      Specified by:
      getProducerName in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The producerName.

    • getProducerNameBytes

      public com.google.protobuf.ByteString getProducerNameBytes()
       The name of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_name = 2;
      Specified by:
      getProducerNameBytes in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The bytes for producerName.

    • setProducerName

      public OnnxMl.ModelProto.Builder setProducerName(String value)
       The name of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_name = 2;
      Parameters:
      value - The producerName to set.
      Returns:
      This builder for chaining.

    • clearProducerName

      public OnnxMl.ModelProto.Builder clearProducerName()
       The name of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_name = 2;
      Returns:
      This builder for chaining.

    • setProducerNameBytes

      public OnnxMl.ModelProto.Builder setProducerNameBytes(com.google.protobuf.ByteString value)
       The name of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_name = 2;
      Parameters:
      value - The bytes for producerName to set.
      Returns:
      This builder for chaining.

    • hasProducerVersion

      public boolean hasProducerVersion()
       The version of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_version = 3;
      Specified by:
      hasProducerVersion in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      Whether the producerVersion field is set.

    • getProducerVersion

      public String getProducerVersion()
       The version of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_version = 3;
      Specified by:
      getProducerVersion in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The producerVersion.

    • getProducerVersionBytes

      public com.google.protobuf.ByteString getProducerVersionBytes()
       The version of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_version = 3;
      Specified by:
      getProducerVersionBytes in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The bytes for producerVersion.

    • setProducerVersion

      public OnnxMl.ModelProto.Builder setProducerVersion(String value)
       The version of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_version = 3;
      Parameters:
      value - The producerVersion to set.
      Returns:
      This builder for chaining.

    • clearProducerVersion

      public OnnxMl.ModelProto.Builder clearProducerVersion()
       The version of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_version = 3;
      Returns:
      This builder for chaining.

    • setProducerVersionBytes

      public OnnxMl.ModelProto.Builder setProducerVersionBytes(com.google.protobuf.ByteString value)
       The version of the framework or tool used to generate this model.
 This field SHOULD be present to indicate which implementation/tool/framework
 emitted the model.
      optional string producer_version = 3;
      Parameters:
      value - The bytes for producerVersion to set.
      Returns:
      This builder for chaining.

    • hasDomain

      public boolean hasDomain()
       Domain name of the model.
 We use reverse domain names as name space indicators. For example:
 `com.facebook.fair` or `com.microsoft.cognitiveservices`
 Together with `model_version` and GraphProto.name, this forms the unique identity of
 the graph.
      optional string domain = 4;
      Specified by:
      hasDomain in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      Whether the domain field is set.

    • getDomain

      public String getDomain()
       Domain name of the model.
 We use reverse domain names as name space indicators. For example:
 `com.facebook.fair` or `com.microsoft.cognitiveservices`
 Together with `model_version` and GraphProto.name, this forms the unique identity of
 the graph.
      optional string domain = 4;
      Specified by:
      getDomain in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The domain.

    • getDomainBytes

      public com.google.protobuf.ByteString getDomainBytes()
       Domain name of the model.
 We use reverse domain names as name space indicators. For example:
 `com.facebook.fair` or `com.microsoft.cognitiveservices`
 Together with `model_version` and GraphProto.name, this forms the unique identity of
 the graph.
      optional string domain = 4;
      Specified by:
      getDomainBytes in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The bytes for domain.

    • setDomain

      public OnnxMl.ModelProto.Builder setDomain(String value)
       Domain name of the model.
 We use reverse domain names as name space indicators. For example:
 `com.facebook.fair` or `com.microsoft.cognitiveservices`
 Together with `model_version` and GraphProto.name, this forms the unique identity of
 the graph.
      optional string domain = 4;
      Parameters:
      value - The domain to set.
      Returns:
      This builder for chaining.

    • clearDomain

      public OnnxMl.ModelProto.Builder clearDomain()
       Domain name of the model.
 We use reverse domain names as name space indicators. For example:
 `com.facebook.fair` or `com.microsoft.cognitiveservices`
 Together with `model_version` and GraphProto.name, this forms the unique identity of
 the graph.
      optional string domain = 4;
      Returns:
      This builder for chaining.

    • setDomainBytes

      public OnnxMl.ModelProto.Builder setDomainBytes(com.google.protobuf.ByteString value)
       Domain name of the model.
 We use reverse domain names as name space indicators. For example:
 `com.facebook.fair` or `com.microsoft.cognitiveservices`
 Together with `model_version` and GraphProto.name, this forms the unique identity of
 the graph.
      optional string domain = 4;
      Parameters:
      value - The bytes for domain to set.
      Returns:
      This builder for chaining.

    • hasModelVersion

      public boolean hasModelVersion()
       The version of the graph encoded. See Version enum below.
      optional int64 model_version = 5;
      Specified by:
      hasModelVersion in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      Whether the modelVersion field is set.

    • getModelVersion

      public long getModelVersion()
       The version of the graph encoded. See Version enum below.
      optional int64 model_version = 5;
      Specified by:
      getModelVersion in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The modelVersion.

    • setModelVersion

      public OnnxMl.ModelProto.Builder setModelVersion(long value)
       The version of the graph encoded. See Version enum below.
      optional int64 model_version = 5;
      Parameters:
      value - The modelVersion to set.
      Returns:
      This builder for chaining.

    • clearModelVersion

      public OnnxMl.ModelProto.Builder clearModelVersion()
       The version of the graph encoded. See Version enum below.
      optional int64 model_version = 5;
      Returns:
      This builder for chaining.

    • hasDocString

      public boolean hasDocString()
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Specified by:
      hasDocString in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      Whether the docString field is set.

    • getDocString

      public String getDocString()
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Specified by:
      getDocString in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The docString.

    • getDocStringBytes

      public com.google.protobuf.ByteString getDocStringBytes()
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Specified by:
      getDocStringBytes in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The bytes for docString.

    • setDocString

      public OnnxMl.ModelProto.Builder setDocString(String value)
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Parameters:
      value - The docString to set.
      Returns:
      This builder for chaining.

    • clearDocString

      public OnnxMl.ModelProto.Builder clearDocString()
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Returns:
      This builder for chaining.

    • setDocStringBytes

      public OnnxMl.ModelProto.Builder setDocStringBytes(com.google.protobuf.ByteString value)
       A human-readable documentation for this model. Markdown is allowed.
      optional string doc_string = 6;
      Parameters:
      value - The bytes for docString to set.
      Returns:
      This builder for chaining.

    • hasGraph

      public boolean hasGraph()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;
      Specified by:
      hasGraph in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      Whether the graph field is set.

    • getGraph

      public OnnxMl.GraphProto getGraph()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;
      Specified by:
      getGraph in interface OnnxMl.ModelProtoOrBuilder
      Returns:
      The graph.

    • setGraph

      public OnnxMl.ModelProto.Builder setGraph(OnnxMl.GraphProto value)
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;

    • setGraph

      public OnnxMl.ModelProto.Builder setGraph(OnnxMl.GraphProto.Builder builderForValue)
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;

    • mergeGraph

      public OnnxMl.ModelProto.Builder mergeGraph(OnnxMl.GraphProto value)
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;

    • clearGraph

      public OnnxMl.ModelProto.Builder clearGraph()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;

    • getGraphBuilder

      public OnnxMl.GraphProto.Builder getGraphBuilder()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;

    • getGraphOrBuilder

      public OnnxMl.GraphProtoOrBuilder getGraphOrBuilder()
       The parameterized graph that is evaluated to execute the model.
      optional .onnx.GraphProto graph = 7;
      Specified by:
      getGraphOrBuilder in interface OnnxMl.ModelProtoOrBuilder

    • getMetadataPropsList

      public List<OnnxMl.StringStringEntryProto> getMetadataPropsList()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;
      Specified by:
      getMetadataPropsList in interface OnnxMl.ModelProtoOrBuilder

    • getMetadataPropsCount

      public int getMetadataPropsCount()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;
      Specified by:
      getMetadataPropsCount in interface OnnxMl.ModelProtoOrBuilder

    • getMetadataProps

      public OnnxMl.StringStringEntryProto getMetadataProps(int index)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;
      Specified by:
      getMetadataProps in interface OnnxMl.ModelProtoOrBuilder

    • setMetadataProps

      public OnnxMl.ModelProto.Builder setMetadataProps(int index, OnnxMl.StringStringEntryProto value)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • setMetadataProps

      public OnnxMl.ModelProto.Builder setMetadataProps(int index, OnnxMl.StringStringEntryProto.Builder builderForValue)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • addMetadataProps

      public OnnxMl.ModelProto.Builder addMetadataProps(OnnxMl.StringStringEntryProto value)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • addMetadataProps

      public OnnxMl.ModelProto.Builder addMetadataProps(int index, OnnxMl.StringStringEntryProto value)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • addMetadataProps

      public OnnxMl.ModelProto.Builder addMetadataProps(OnnxMl.StringStringEntryProto.Builder builderForValue)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • addMetadataProps

      public OnnxMl.ModelProto.Builder addMetadataProps(int index, OnnxMl.StringStringEntryProto.Builder builderForValue)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • addAllMetadataProps

      public OnnxMl.ModelProto.Builder addAllMetadataProps(Iterable<? extends OnnxMl.StringStringEntryProto> values)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • clearMetadataProps

      public OnnxMl.ModelProto.Builder clearMetadataProps()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • removeMetadataProps

      public OnnxMl.ModelProto.Builder removeMetadataProps(int index)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getMetadataPropsBuilder

      public OnnxMl.StringStringEntryProto.Builder getMetadataPropsBuilder(int index)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getMetadataPropsOrBuilder

      public OnnxMl.StringStringEntryProtoOrBuilder getMetadataPropsOrBuilder(int index)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;
      Specified by:
      getMetadataPropsOrBuilder in interface OnnxMl.ModelProtoOrBuilder

    • getMetadataPropsOrBuilderList

      public List<? extends OnnxMl.StringStringEntryProtoOrBuilder> getMetadataPropsOrBuilderList()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;
      Specified by:
      getMetadataPropsOrBuilderList in interface OnnxMl.ModelProtoOrBuilder

    • addMetadataPropsBuilder

      public OnnxMl.StringStringEntryProto.Builder addMetadataPropsBuilder()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • addMetadataPropsBuilder

      public OnnxMl.StringStringEntryProto.Builder addMetadataPropsBuilder(int index)
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getMetadataPropsBuilderList

      public List<OnnxMl.StringStringEntryProto.Builder> getMetadataPropsBuilderList()
       Named metadata values; keys should be distinct.
      repeated .onnx.StringStringEntryProto metadata_props = 14;

    • getTrainingInfoList

      public List<OnnxMl.TrainingInfoProto> getTrainingInfoList()
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;
      Specified by:
      getTrainingInfoList in interface OnnxMl.ModelProtoOrBuilder

    • getTrainingInfoCount

      public int getTrainingInfoCount()
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;
      Specified by:
      getTrainingInfoCount in interface OnnxMl.ModelProtoOrBuilder

    • getTrainingInfo

      public OnnxMl.TrainingInfoProto getTrainingInfo(int index)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;
      Specified by:
      getTrainingInfo in interface OnnxMl.ModelProtoOrBuilder

    • setTrainingInfo

      public OnnxMl.ModelProto.Builder setTrainingInfo(int index, OnnxMl.TrainingInfoProto value)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • setTrainingInfo

      public OnnxMl.ModelProto.Builder setTrainingInfo(int index, OnnxMl.TrainingInfoProto.Builder builderForValue)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • addTrainingInfo

      public OnnxMl.ModelProto.Builder addTrainingInfo(OnnxMl.TrainingInfoProto value)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • addTrainingInfo

      public OnnxMl.ModelProto.Builder addTrainingInfo(int index, OnnxMl.TrainingInfoProto value)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • addTrainingInfo

      public OnnxMl.ModelProto.Builder addTrainingInfo(OnnxMl.TrainingInfoProto.Builder builderForValue)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • addTrainingInfo

      public OnnxMl.ModelProto.Builder addTrainingInfo(int index, OnnxMl.TrainingInfoProto.Builder builderForValue)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • addAllTrainingInfo

      public OnnxMl.ModelProto.Builder addAllTrainingInfo(Iterable<? extends OnnxMl.TrainingInfoProto> values)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • clearTrainingInfo

      public OnnxMl.ModelProto.Builder clearTrainingInfo()
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • removeTrainingInfo

      public OnnxMl.ModelProto.Builder removeTrainingInfo(int index)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • getTrainingInfoBuilder

      public OnnxMl.TrainingInfoProto.Builder getTrainingInfoBuilder(int index)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • getTrainingInfoOrBuilder

      public OnnxMl.TrainingInfoProtoOrBuilder getTrainingInfoOrBuilder(int index)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;
      Specified by:
      getTrainingInfoOrBuilder in interface OnnxMl.ModelProtoOrBuilder

    • getTrainingInfoOrBuilderList

      public List<? extends OnnxMl.TrainingInfoProtoOrBuilder> getTrainingInfoOrBuilderList()
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;
      Specified by:
      getTrainingInfoOrBuilderList in interface OnnxMl.ModelProtoOrBuilder

    • addTrainingInfoBuilder

      public OnnxMl.TrainingInfoProto.Builder addTrainingInfoBuilder()
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • addTrainingInfoBuilder

      public OnnxMl.TrainingInfoProto.Builder addTrainingInfoBuilder(int index)
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • getTrainingInfoBuilderList

      public List<OnnxMl.TrainingInfoProto.Builder> getTrainingInfoBuilderList()
       Training-specific information. Sequentially executing all stored
 `TrainingInfoProto.algorithm`s and assigning their outputs following
 the corresponding `TrainingInfoProto.update_binding`s is one training
 iteration. Similarly, to initialize the model
 (as if training hasn't happened), the user should sequentially execute
 all stored `TrainingInfoProto.initialization`s and assigns their outputs
 using `TrainingInfoProto.initialization_binding`s.
 If this field is empty, the training behavior of the model is undefined.
      repeated .onnx.TrainingInfoProto training_info = 20;

    • setUnknownFields

      public final OnnxMl.ModelProto.Builder setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
      Specified by:
      setUnknownFields in interface com.google.protobuf.Message.Builder
      Overrides:
      setUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>

    • mergeUnknownFields

      public final OnnxMl.ModelProto.Builder mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
      Specified by:
      mergeUnknownFields in interface com.google.protobuf.Message.Builder
      Overrides:
      mergeUnknownFields in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.ModelProto.Builder>