ai.onnx.proto

Class OnnxMl.SparseTensorProto.Builder

java.lang.Object

com.google.protobuf.AbstractMessageLite.Builder

com.google.protobuf.AbstractMessage.Builder<BuilderType>

com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.SparseTensorProto.Builder>

ai.onnx.proto.OnnxMl.SparseTensorProto.Builder

All Implemented Interfaces:

OnnxMl.SparseTensorProtoOrBuilder, com.google.protobuf.Message.Builder, com.google.protobuf.MessageLite.Builder, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, java.lang.Cloneable

Enclosing class:

OnnxMl.SparseTensorProto

public static final class OnnxMl.SparseTensorProto.Builder
extends com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.SparseTensorProto.Builder>
implements OnnxMl.SparseTensorProtoOrBuilder

 A serialized sparse-tensor value
 

Protobuf type onnx.SparseTensorProto

Method Summary

Modifier and Type	Method and Description
OnnxMl.SparseTensorProto.Builder	addAllDims(java.lang.Iterable<? extends java.lang.Long> values) The shape of the underlying dense-tensor: [dim_1, dim_2, ...
OnnxMl.SparseTensorProto.Builder	addDims(long value) The shape of the underlying dense-tensor: [dim_1, dim_2, ...
OnnxMl.SparseTensorProto.Builder	addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
OnnxMl.SparseTensorProto	build()
OnnxMl.SparseTensorProto	buildPartial()
OnnxMl.SparseTensorProto.Builder	clear()
OnnxMl.SparseTensorProto.Builder	clearDims() The shape of the underlying dense-tensor: [dim_1, dim_2, ...
OnnxMl.SparseTensorProto.Builder	clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
OnnxMl.SparseTensorProto.Builder	clearIndices() The indices of the non-default values, which may be stored in one of two formats.
OnnxMl.SparseTensorProto.Builder	clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
OnnxMl.SparseTensorProto.Builder	clearValues() The sequence of non-default values are encoded as a tensor of shape [NNZ].
OnnxMl.SparseTensorProto.Builder	clone()
OnnxMl.SparseTensorProto	getDefaultInstanceForType()
static com.google.protobuf.Descriptors.Descriptor	getDescriptor()
com.google.protobuf.Descriptors.Descriptor	getDescriptorForType()
long	getDims(int index) The shape of the underlying dense-tensor: [dim_1, dim_2, ...
int	getDimsCount() The shape of the underlying dense-tensor: [dim_1, dim_2, ...
java.util.List<java.lang.Long>	getDimsList() The shape of the underlying dense-tensor: [dim_1, dim_2, ...
OnnxMl.TensorProto	getIndices() The indices of the non-default values, which may be stored in one of two formats.
OnnxMl.TensorProto.Builder	getIndicesBuilder() The indices of the non-default values, which may be stored in one of two formats.
OnnxMl.TensorProtoOrBuilder	getIndicesOrBuilder() The indices of the non-default values, which may be stored in one of two formats.
OnnxMl.TensorProto	getValues() The sequence of non-default values are encoded as a tensor of shape [NNZ].
OnnxMl.TensorProto.Builder	getValuesBuilder() The sequence of non-default values are encoded as a tensor of shape [NNZ].
OnnxMl.TensorProtoOrBuilder	getValuesOrBuilder() The sequence of non-default values are encoded as a tensor of shape [NNZ].
boolean	hasIndices() The indices of the non-default values, which may be stored in one of two formats.
boolean	hasValues() The sequence of non-default values are encoded as a tensor of shape [NNZ].
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable	internalGetFieldAccessorTable()
boolean	isInitialized()
OnnxMl.SparseTensorProto.Builder	mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
OnnxMl.SparseTensorProto.Builder	mergeFrom(com.google.protobuf.Message other)
OnnxMl.SparseTensorProto.Builder	mergeFrom(OnnxMl.SparseTensorProto other)
OnnxMl.SparseTensorProto.Builder	mergeIndices(OnnxMl.TensorProto value) The indices of the non-default values, which may be stored in one of two formats.
OnnxMl.SparseTensorProto.Builder	mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
OnnxMl.SparseTensorProto.Builder	mergeValues(OnnxMl.TensorProto value) The sequence of non-default values are encoded as a tensor of shape [NNZ].
OnnxMl.SparseTensorProto.Builder	setDims(int index, long value) The shape of the underlying dense-tensor: [dim_1, dim_2, ...
OnnxMl.SparseTensorProto.Builder	setField(com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value)
OnnxMl.SparseTensorProto.Builder	setIndices(OnnxMl.TensorProto.Builder builderForValue) The indices of the non-default values, which may be stored in one of two formats.
OnnxMl.SparseTensorProto.Builder	setIndices(OnnxMl.TensorProto value) The indices of the non-default values, which may be stored in one of two formats.
OnnxMl.SparseTensorProto.Builder	setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value)
OnnxMl.SparseTensorProto.Builder	setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
OnnxMl.SparseTensorProto.Builder	setValues(OnnxMl.TensorProto.Builder builderForValue) The sequence of non-default values are encoded as a tensor of shape [NNZ].
OnnxMl.SparseTensorProto.Builder	setValues(OnnxMl.TensorProto value) The sequence of non-default values are encoded as a tensor of shape [NNZ].

Methods inherited from class com.google.protobuf.GeneratedMessageV3.Builder

getAllFields, getField, getFieldBuilder, getOneofFieldDescriptor, getParentForChildren, getRepeatedField, getRepeatedFieldBuilder, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof, internalGetMapField, internalGetMutableMapField, isClean, markClean, newBuilderForField, onBuilt, onChanged, setUnknownFieldsProto3

Methods inherited from class com.google.protobuf.AbstractMessage.Builder

findInitializationErrors, getInitializationErrorString, internalMergeFrom, mergeDelimitedFrom, mergeDelimitedFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, mergeFrom, newUninitializedMessageException, toString

Methods inherited from class com.google.protobuf.AbstractMessageLite.Builder

addAll, addAll, mergeFrom, newUninitializedMessageException

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface com.google.protobuf.MessageOrBuilder

findInitializationErrors, getAllFields, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof

Methods inherited from interface com.google.protobuf.MessageLite.Builder

mergeFrom

Method Detail

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.SparseTensorProto.Builder>

clear

public OnnxMl.SparseTensorProto.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.SparseTensorProto.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.SparseTensorProto.Builder>

getDefaultInstanceForType

public OnnxMl.SparseTensorProto getDefaultInstanceForType()

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder

Specified by:

getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder

build

public OnnxMl.SparseTensorProto build()

Specified by:

build in interface com.google.protobuf.Message.Builder

Specified by:

build in interface com.google.protobuf.MessageLite.Builder

buildPartial

public OnnxMl.SparseTensorProto buildPartial()

Specified by:

buildPartial in interface com.google.protobuf.Message.Builder

Specified by:

buildPartial in interface com.google.protobuf.MessageLite.Builder

clone

public OnnxMl.SparseTensorProto.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.SparseTensorProto.Builder>

setField

public OnnxMl.SparseTensorProto.Builder setField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                 java.lang.Object value)

Specified by:

setField in interface com.google.protobuf.Message.Builder

Overrides:

setField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.SparseTensorProto.Builder>

clearField

public OnnxMl.SparseTensorProto.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.SparseTensorProto.Builder>

clearOneof

public OnnxMl.SparseTensorProto.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.SparseTensorProto.Builder>

setRepeatedField

public OnnxMl.SparseTensorProto.Builder setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                         int index,
                                                         java.lang.Object value)

Specified by:

setRepeatedField in interface com.google.protobuf.Message.Builder

Overrides:

setRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.SparseTensorProto.Builder>

addRepeatedField

public OnnxMl.SparseTensorProto.Builder addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field,
                                                         java.lang.Object value)

Specified by:

addRepeatedField in interface com.google.protobuf.Message.Builder

Overrides:

addRepeatedField in class com.google.protobuf.GeneratedMessageV3.Builder<OnnxMl.SparseTensorProto.Builder>

mergeFrom

public OnnxMl.SparseTensorProto.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.SparseTensorProto.Builder>

mergeFrom

public OnnxMl.SparseTensorProto.Builder mergeFrom(OnnxMl.SparseTensorProto 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.SparseTensorProto.Builder>

mergeFrom

public OnnxMl.SparseTensorProto.Builder mergeFrom(com.google.protobuf.CodedInputStream input,
                                                  com.google.protobuf.ExtensionRegistryLite extensionRegistry)
                                           throws java.io.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.SparseTensorProto.Builder>

Throws:

java.io.IOException

hasValues

public boolean hasValues()

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

Specified by:

hasValues in interface OnnxMl.SparseTensorProtoOrBuilder

Returns:

Whether the values field is set.

getValues

public OnnxMl.TensorProto getValues()

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

Specified by:

getValues in interface OnnxMl.SparseTensorProtoOrBuilder

Returns:

The values.

setValues

public OnnxMl.SparseTensorProto.Builder setValues(OnnxMl.TensorProto value)

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

setValues

public OnnxMl.SparseTensorProto.Builder setValues(OnnxMl.TensorProto.Builder builderForValue)

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

mergeValues

public OnnxMl.SparseTensorProto.Builder mergeValues(OnnxMl.TensorProto value)

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

clearValues

public OnnxMl.SparseTensorProto.Builder clearValues()

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

getValuesBuilder

public OnnxMl.TensorProto.Builder getValuesBuilder()

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

getValuesOrBuilder

public OnnxMl.TensorProtoOrBuilder getValuesOrBuilder()

 The sequence of non-default values are encoded as a tensor of shape [NNZ].
 The default-value is zero for numeric tensors, and empty-string for string tensors.
 values must have a non-empty name present which serves as a name for SparseTensorProto
 when used in sparse_initializer list.
 

optional .onnx.TensorProto values = 1;

Specified by:

getValuesOrBuilder in interface OnnxMl.SparseTensorProtoOrBuilder

hasIndices

public boolean hasIndices()

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

Specified by:

hasIndices in interface OnnxMl.SparseTensorProtoOrBuilder

Returns:

Whether the indices field is set.

getIndices

public OnnxMl.TensorProto getIndices()

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

Specified by:

getIndices in interface OnnxMl.SparseTensorProtoOrBuilder

Returns:

The indices.

setIndices

public OnnxMl.SparseTensorProto.Builder setIndices(OnnxMl.TensorProto value)

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

setIndices

public OnnxMl.SparseTensorProto.Builder setIndices(OnnxMl.TensorProto.Builder builderForValue)

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

mergeIndices

public OnnxMl.SparseTensorProto.Builder mergeIndices(OnnxMl.TensorProto value)

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

clearIndices

public OnnxMl.SparseTensorProto.Builder clearIndices()

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

getIndicesBuilder

public OnnxMl.TensorProto.Builder getIndicesBuilder()

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

getIndicesOrBuilder

public OnnxMl.TensorProtoOrBuilder getIndicesOrBuilder()

 The indices of the non-default values, which may be stored in one of two formats.
 (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value
 corresponding to the j-th index of the i-th value (in the values tensor).
 (b) Indices can be a tensor of shape [NNZ], in which case the i-th value
 must be the linearized-index of the i-th value (in the values tensor).
 The linearized-index can be converted into an index tuple (k_1,...,k_rank)
 using the shape provided below.
 The indices must appear in ascending order without duplication.
 In the first format, the ordering is lexicographic-ordering:
 e.g., index-value [1,4] must appear before [2,1]
 

optional .onnx.TensorProto indices = 2;

Specified by:

getIndicesOrBuilder in interface OnnxMl.SparseTensorProtoOrBuilder

getDimsList

public java.util.List<java.lang.Long> getDimsList()

 The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
 

repeated int64 dims = 3;

Specified by:

getDimsList in interface OnnxMl.SparseTensorProtoOrBuilder

Returns:

A list containing the dims.

getDimsCount

public int getDimsCount()

 The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
 

repeated int64 dims = 3;

Specified by:

getDimsCount in interface OnnxMl.SparseTensorProtoOrBuilder

Returns:

The count of dims.

getDims

public long getDims(int index)

 The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
 

repeated int64 dims = 3;

Specified by:

getDims in interface OnnxMl.SparseTensorProtoOrBuilder

Parameters:

index - The index of the element to return.

Returns:

The dims at the given index.

setDims

public OnnxMl.SparseTensorProto.Builder setDims(int index,
                                                long value)

 The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
 

repeated int64 dims = 3;

Parameters:

index - The index to set the value at.

value - The dims to set.

Returns:

This builder for chaining.

addDims

public OnnxMl.SparseTensorProto.Builder addDims(long value)

 The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
 

repeated int64 dims = 3;

Parameters:

value - The dims to add.

Returns:

This builder for chaining.

addAllDims

public OnnxMl.SparseTensorProto.Builder addAllDims(java.lang.Iterable<? extends java.lang.Long> values)

 The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
 

repeated int64 dims = 3;

Parameters:

values - The dims to add.

Returns:

This builder for chaining.

clearDims

public OnnxMl.SparseTensorProto.Builder clearDims()

 The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]
 

repeated int64 dims = 3;

Returns:

This builder for chaining.

setUnknownFields

public final OnnxMl.SparseTensorProto.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.SparseTensorProto.Builder>

mergeUnknownFields

public final OnnxMl.SparseTensorProto.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.SparseTensorProto.Builder>