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// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: tensorflow_metadata/proto/v0/metric.proto

// Protobuf Java Version: 3.25.4
package org.tensorflow.metadata.v0;

/**
 * Protobuf type {@code tensorflow.metadata.v0.FalsePositiveRateAtThreshold}
 */
public final class FalsePositiveRateAtThreshold extends
    com.google.protobuf.GeneratedMessageV3 implements
    // @@protoc_insertion_point(message_implements:tensorflow.metadata.v0.FalsePositiveRateAtThreshold)
    FalsePositiveRateAtThresholdOrBuilder {
private static final long serialVersionUID = 0L;
  // Use FalsePositiveRateAtThreshold.newBuilder() to construct.
  private FalsePositiveRateAtThreshold(com.google.protobuf.GeneratedMessageV3.Builder builder) {
    super(builder);
  }
  private FalsePositiveRateAtThreshold() {
  }

  @java.lang.Override
  @SuppressWarnings({"unused"})
  protected java.lang.Object newInstance(
      UnusedPrivateParameter unused) {
    return new FalsePositiveRateAtThreshold();
  }

  public static final com.google.protobuf.Descriptors.Descriptor
      getDescriptor() {
    return org.tensorflow.metadata.v0.Metric.internal_static_tensorflow_metadata_v0_FalsePositiveRateAtThreshold_descriptor;
  }

  @java.lang.Override
  protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
      internalGetFieldAccessorTable() {
    return org.tensorflow.metadata.v0.Metric.internal_static_tensorflow_metadata_v0_FalsePositiveRateAtThreshold_fieldAccessorTable
        .ensureFieldAccessorsInitialized(
            org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold.class, org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold.Builder.class);
  }

  private int bitField0_;
  public static final int THRESHOLD_FIELD_NUMBER = 1;
  private com.google.protobuf.DoubleValue threshold_;
  /**
   * 
   * Threshold to apply to a prediction to determine positive vs negative.
   * Note: if the model is calibrated, the threshold can be thought of as a
   * probability so the threshold has a stable, intuitive semantic.
   * However, not all solutions may be calibrated, and not all computations of
   * the metric may operate on a calibrated score. In AutoTFX, the final model
   * metrics are computed on a calibrated score, but the metrics computed within
   * the model selection process are uncalibrated. Be aware of this possible
   * skew in the metrics between model selection and final model evaluation.
   * 
* * .google.protobuf.DoubleValue threshold = 1; * @return Whether the threshold field is set. */ @java.lang.Override public boolean hasThreshold() { return ((bitField0_ & 0x00000001) != 0); } /** *
   * Threshold to apply to a prediction to determine positive vs negative.
   * Note: if the model is calibrated, the threshold can be thought of as a
   * probability so the threshold has a stable, intuitive semantic.
   * However, not all solutions may be calibrated, and not all computations of
   * the metric may operate on a calibrated score. In AutoTFX, the final model
   * metrics are computed on a calibrated score, but the metrics computed within
   * the model selection process are uncalibrated. Be aware of this possible
   * skew in the metrics between model selection and final model evaluation.
   * 
* * .google.protobuf.DoubleValue threshold = 1; * @return The threshold. */ @java.lang.Override public com.google.protobuf.DoubleValue getThreshold() { return threshold_ == null ? com.google.protobuf.DoubleValue.getDefaultInstance() : threshold_; } /** *
   * Threshold to apply to a prediction to determine positive vs negative.
   * Note: if the model is calibrated, the threshold can be thought of as a
   * probability so the threshold has a stable, intuitive semantic.
   * However, not all solutions may be calibrated, and not all computations of
   * the metric may operate on a calibrated score. In AutoTFX, the final model
   * metrics are computed on a calibrated score, but the metrics computed within
   * the model selection process are uncalibrated. Be aware of this possible
   * skew in the metrics between model selection and final model evaluation.
   * 
* * .google.protobuf.DoubleValue threshold = 1; */ @java.lang.Override public com.google.protobuf.DoubleValueOrBuilder getThresholdOrBuilder() { return threshold_ == null ? com.google.protobuf.DoubleValue.getDefaultInstance() : threshold_; } private byte memoizedIsInitialized = -1; @java.lang.Override public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; if (isInitialized == 1) return true; if (isInitialized == 0) return false; memoizedIsInitialized = 1; return true; } @java.lang.Override public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException { if (((bitField0_ & 0x00000001) != 0)) { output.writeMessage(1, getThreshold()); } getUnknownFields().writeTo(output); } @java.lang.Override public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; if (((bitField0_ & 0x00000001) != 0)) { size += com.google.protobuf.CodedOutputStream .computeMessageSize(1, getThreshold()); } size += getUnknownFields().getSerializedSize(); memoizedSize = size; return size; } @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold)) { return super.equals(obj); } org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold other = (org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold) obj; if (hasThreshold() != other.hasThreshold()) return false; if (hasThreshold()) { if (!getThreshold() .equals(other.getThreshold())) return false; } if (!getUnknownFields().equals(other.getUnknownFields())) return false; return true; } @java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); if (hasThreshold()) { hash = (37 * hash) + THRESHOLD_FIELD_NUMBER; hash = (53 * hash) + getThreshold().hashCode(); } hash = (29 * hash) + getUnknownFields().hashCode(); memoizedHashCode = hash; return hash; } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseDelimitedFrom( java.io.InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( com.google.protobuf.CodedInputStream input) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold parseFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return com.google.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } @java.lang.Override public Builder newBuilderForType() { return newBuilder(); } public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } public static Builder newBuilder(org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold prototype) { return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype); } @java.lang.Override public Builder toBuilder() { return this == DEFAULT_INSTANCE ? new Builder() : new Builder().mergeFrom(this); } @java.lang.Override protected Builder newBuilderForType( com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { Builder builder = new Builder(parent); return builder; } /** * Protobuf type {@code tensorflow.metadata.v0.FalsePositiveRateAtThreshold} */ public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorflow.metadata.v0.FalsePositiveRateAtThreshold) org.tensorflow.metadata.v0.FalsePositiveRateAtThresholdOrBuilder { public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.metadata.v0.Metric.internal_static_tensorflow_metadata_v0_FalsePositiveRateAtThreshold_descriptor; } @java.lang.Override protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.metadata.v0.Metric.internal_static_tensorflow_metadata_v0_FalsePositiveRateAtThreshold_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold.class, org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold.Builder.class); } // Construct using org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold.newBuilder() private Builder() { maybeForceBuilderInitialization(); } private Builder( com.google.protobuf.GeneratedMessageV3.BuilderParent parent) { super(parent); maybeForceBuilderInitialization(); } private void maybeForceBuilderInitialization() { if (com.google.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { getThresholdFieldBuilder(); } } @java.lang.Override public Builder clear() { super.clear(); bitField0_ = 0; threshold_ = null; if (thresholdBuilder_ != null) { thresholdBuilder_.dispose(); thresholdBuilder_ = null; } return this; } @java.lang.Override public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() { return org.tensorflow.metadata.v0.Metric.internal_static_tensorflow_metadata_v0_FalsePositiveRateAtThreshold_descriptor; } @java.lang.Override public org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold getDefaultInstanceForType() { return org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold.getDefaultInstance(); } @java.lang.Override public org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold build() { org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } @java.lang.Override public org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold buildPartial() { org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold result = new org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold(this); if (bitField0_ != 0) { buildPartial0(result); } onBuilt(); return result; } private void buildPartial0(org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold result) { int from_bitField0_ = bitField0_; int to_bitField0_ = 0; if (((from_bitField0_ & 0x00000001) != 0)) { result.threshold_ = thresholdBuilder_ == null ? threshold_ : thresholdBuilder_.build(); to_bitField0_ |= 0x00000001; } result.bitField0_ |= to_bitField0_; } @java.lang.Override public Builder clone() { return super.clone(); } @java.lang.Override public Builder setField( com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return super.setField(field, value); } @java.lang.Override public Builder clearField( com.google.protobuf.Descriptors.FieldDescriptor field) { return super.clearField(field); } @java.lang.Override public Builder clearOneof( com.google.protobuf.Descriptors.OneofDescriptor oneof) { return super.clearOneof(oneof); } @java.lang.Override public Builder setRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) { return super.setRepeatedField(field, index, value); } @java.lang.Override public Builder addRepeatedField( com.google.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return super.addRepeatedField(field, value); } @java.lang.Override public Builder mergeFrom(com.google.protobuf.Message other) { if (other instanceof org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold) { return mergeFrom((org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold)other); } else { super.mergeFrom(other); return this; } } public Builder mergeFrom(org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold other) { if (other == org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold.getDefaultInstance()) return this; if (other.hasThreshold()) { mergeThreshold(other.getThreshold()); } this.mergeUnknownFields(other.getUnknownFields()); onChanged(); return this; } @java.lang.Override public final boolean isInitialized() { return true; } @java.lang.Override public Builder mergeFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { if (extensionRegistry == null) { throw new java.lang.NullPointerException(); } try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; case 10: { input.readMessage( getThresholdFieldBuilder().getBuilder(), extensionRegistry); bitField0_ |= 0x00000001; break; } // case 10 default: { if (!super.parseUnknownField(input, extensionRegistry, tag)) { done = true; // was an endgroup tag } break; } // default: } // switch (tag) } // while (!done) } catch (com.google.protobuf.InvalidProtocolBufferException e) { throw e.unwrapIOException(); } finally { onChanged(); } // finally return this; } private int bitField0_; private com.google.protobuf.DoubleValue threshold_; private com.google.protobuf.SingleFieldBuilderV3< com.google.protobuf.DoubleValue, com.google.protobuf.DoubleValue.Builder, com.google.protobuf.DoubleValueOrBuilder> thresholdBuilder_; /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; * @return Whether the threshold field is set. */ public boolean hasThreshold() { return ((bitField0_ & 0x00000001) != 0); } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; * @return The threshold. */ public com.google.protobuf.DoubleValue getThreshold() { if (thresholdBuilder_ == null) { return threshold_ == null ? com.google.protobuf.DoubleValue.getDefaultInstance() : threshold_; } else { return thresholdBuilder_.getMessage(); } } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; */ public Builder setThreshold(com.google.protobuf.DoubleValue value) { if (thresholdBuilder_ == null) { if (value == null) { throw new NullPointerException(); } threshold_ = value; } else { thresholdBuilder_.setMessage(value); } bitField0_ |= 0x00000001; onChanged(); return this; } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; */ public Builder setThreshold( com.google.protobuf.DoubleValue.Builder builderForValue) { if (thresholdBuilder_ == null) { threshold_ = builderForValue.build(); } else { thresholdBuilder_.setMessage(builderForValue.build()); } bitField0_ |= 0x00000001; onChanged(); return this; } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; */ public Builder mergeThreshold(com.google.protobuf.DoubleValue value) { if (thresholdBuilder_ == null) { if (((bitField0_ & 0x00000001) != 0) && threshold_ != null && threshold_ != com.google.protobuf.DoubleValue.getDefaultInstance()) { getThresholdBuilder().mergeFrom(value); } else { threshold_ = value; } } else { thresholdBuilder_.mergeFrom(value); } if (threshold_ != null) { bitField0_ |= 0x00000001; onChanged(); } return this; } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; */ public Builder clearThreshold() { bitField0_ = (bitField0_ & ~0x00000001); threshold_ = null; if (thresholdBuilder_ != null) { thresholdBuilder_.dispose(); thresholdBuilder_ = null; } onChanged(); return this; } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; */ public com.google.protobuf.DoubleValue.Builder getThresholdBuilder() { bitField0_ |= 0x00000001; onChanged(); return getThresholdFieldBuilder().getBuilder(); } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; */ public com.google.protobuf.DoubleValueOrBuilder getThresholdOrBuilder() { if (thresholdBuilder_ != null) { return thresholdBuilder_.getMessageOrBuilder(); } else { return threshold_ == null ? com.google.protobuf.DoubleValue.getDefaultInstance() : threshold_; } } /** *
     * Threshold to apply to a prediction to determine positive vs negative.
     * Note: if the model is calibrated, the threshold can be thought of as a
     * probability so the threshold has a stable, intuitive semantic.
     * However, not all solutions may be calibrated, and not all computations of
     * the metric may operate on a calibrated score. In AutoTFX, the final model
     * metrics are computed on a calibrated score, but the metrics computed within
     * the model selection process are uncalibrated. Be aware of this possible
     * skew in the metrics between model selection and final model evaluation.
     * 
* * .google.protobuf.DoubleValue threshold = 1; */ private com.google.protobuf.SingleFieldBuilderV3< com.google.protobuf.DoubleValue, com.google.protobuf.DoubleValue.Builder, com.google.protobuf.DoubleValueOrBuilder> getThresholdFieldBuilder() { if (thresholdBuilder_ == null) { thresholdBuilder_ = new com.google.protobuf.SingleFieldBuilderV3< com.google.protobuf.DoubleValue, com.google.protobuf.DoubleValue.Builder, com.google.protobuf.DoubleValueOrBuilder>( getThreshold(), getParentForChildren(), isClean()); threshold_ = null; } return thresholdBuilder_; } @java.lang.Override public final Builder setUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { return super.setUnknownFields(unknownFields); } @java.lang.Override public final Builder mergeUnknownFields( final com.google.protobuf.UnknownFieldSet unknownFields) { return super.mergeUnknownFields(unknownFields); } // @@protoc_insertion_point(builder_scope:tensorflow.metadata.v0.FalsePositiveRateAtThreshold) } // @@protoc_insertion_point(class_scope:tensorflow.metadata.v0.FalsePositiveRateAtThreshold) private static final org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold DEFAULT_INSTANCE; static { DEFAULT_INSTANCE = new org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold(); } public static org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold getDefaultInstance() { return DEFAULT_INSTANCE; } private static final com.google.protobuf.Parser PARSER = new com.google.protobuf.AbstractParser() { @java.lang.Override public FalsePositiveRateAtThreshold parsePartialFrom( com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException { Builder builder = newBuilder(); try { builder.mergeFrom(input, extensionRegistry); } catch (com.google.protobuf.InvalidProtocolBufferException e) { throw e.setUnfinishedMessage(builder.buildPartial()); } catch (com.google.protobuf.UninitializedMessageException e) { throw e.asInvalidProtocolBufferException().setUnfinishedMessage(builder.buildPartial()); } catch (java.io.IOException e) { throw new com.google.protobuf.InvalidProtocolBufferException(e) .setUnfinishedMessage(builder.buildPartial()); } return builder.buildPartial(); } }; public static com.google.protobuf.Parser parser() { return PARSER; } @java.lang.Override public com.google.protobuf.Parser getParserForType() { return PARSER; } @java.lang.Override public org.tensorflow.metadata.v0.FalsePositiveRateAtThreshold getDefaultInstanceForType() { return DEFAULT_INSTANCE; } }




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