// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: google/cloud/automl/v1beta1/tables.proto

package com.google.cloud.automl.v1beta1;

/**
 *
 *
 * <pre>
 * Contains annotation details specific to Tables.
 * </pre>
 *
 * Protobuf type {@code google.cloud.automl.v1beta1.TablesAnnotation}
 */
public final class TablesAnnotation extends com.google.protobuf.GeneratedMessageV3
    implements
    // @@protoc_insertion_point(message_implements:google.cloud.automl.v1beta1.TablesAnnotation)
    TablesAnnotationOrBuilder {
  private static final long serialVersionUID = 0L;
  // Use TablesAnnotation.newBuilder() to construct.
  private TablesAnnotation(com.google.protobuf.GeneratedMessageV3.Builder<?> builder) {
    super(builder);
  }

  private TablesAnnotation() {
    tablesModelColumnInfo_ = java.util.Collections.emptyList();
  }

  @java.lang.Override
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    return this.unknownFields;
  }

  private TablesAnnotation(
      com.google.protobuf.CodedInputStream input,
      com.google.protobuf.ExtensionRegistryLite extensionRegistry)
      throws com.google.protobuf.InvalidProtocolBufferException {
    this();
    if (extensionRegistry == null) {
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        switch (tag) {
          case 0:
            done = true;
            break;
          case 13:
            {
              score_ = input.readFloat();
              break;
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                value_ = subBuilder.buildPartial();
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          case 26:
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                tablesModelColumnInfo_ =
                    new java.util.ArrayList<
                        com.google.cloud.automl.v1beta1.TablesModelColumnInfo>();
                mutable_bitField0_ |= 0x00000008;
              }
              tablesModelColumnInfo_.add(
                  input.readMessage(
                      com.google.cloud.automl.v1beta1.TablesModelColumnInfo.parser(),
                      extensionRegistry));
              break;
            }
          case 34:
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              com.google.cloud.automl.v1beta1.DoubleRange.Builder subBuilder = null;
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              }
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                  input.readMessage(
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  public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
    return com.google.cloud.automl.v1beta1.Tables
        .internal_static_google_cloud_automl_v1beta1_TablesAnnotation_descriptor;
  }

  @java.lang.Override
  protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
      internalGetFieldAccessorTable() {
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            com.google.cloud.automl.v1beta1.TablesAnnotation.Builder.class);
  }

  private int bitField0_;
  public static final int SCORE_FIELD_NUMBER = 1;
  private float score_;
  /**
   *
   *
   * <pre>
   * Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher
   * value means greater confidence in the returned value.
   * For
   * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * of ARRAY(CATEGORY) data type, this is a confidence that one of the values
   * in the ARRAY would be the provided value.
   * For
   * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * of FLOAT64 data type the score is not populated.
   * </pre>
   *
   * <code>float score = 1;</code>
   */
  public float getScore() {
    return score_;
  }

  public static final int PREDICTION_INTERVAL_FIELD_NUMBER = 4;
  private com.google.cloud.automl.v1beta1.DoubleRange predictionInterval_;
  /**
   *
   *
   * <pre>
   * Output only. Only populated when
   * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * has FLOAT64 data type (i.e. for regression predictions). An interval in
   * which the exactly correct target value has 95% chance to be in.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
   */
  public boolean hasPredictionInterval() {
    return predictionInterval_ != null;
  }
  /**
   *
   *
   * <pre>
   * Output only. Only populated when
   * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * has FLOAT64 data type (i.e. for regression predictions). An interval in
   * which the exactly correct target value has 95% chance to be in.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
   */
  public com.google.cloud.automl.v1beta1.DoubleRange getPredictionInterval() {
    return predictionInterval_ == null
        ? com.google.cloud.automl.v1beta1.DoubleRange.getDefaultInstance()
        : predictionInterval_;
  }
  /**
   *
   *
   * <pre>
   * Output only. Only populated when
   * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * has FLOAT64 data type (i.e. for regression predictions). An interval in
   * which the exactly correct target value has 95% chance to be in.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
   */
  public com.google.cloud.automl.v1beta1.DoubleRangeOrBuilder getPredictionIntervalOrBuilder() {
    return getPredictionInterval();
  }

  public static final int VALUE_FIELD_NUMBER = 2;
  private com.google.protobuf.Value value_;
  /**
   *
   *
   * <pre>
   * The predicted value of the row's
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
   * The value depends on the column's DataType:
   * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
   *            value.
   * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
   * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
   *                   ARRAY in that column (with the above confidence `score`).
   * </pre>
   *
   * <code>.google.protobuf.Value value = 2;</code>
   */
  public boolean hasValue() {
    return value_ != null;
  }
  /**
   *
   *
   * <pre>
   * The predicted value of the row's
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
   * The value depends on the column's DataType:
   * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
   *            value.
   * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
   * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
   *                   ARRAY in that column (with the above confidence `score`).
   * </pre>
   *
   * <code>.google.protobuf.Value value = 2;</code>
   */
  public com.google.protobuf.Value getValue() {
    return value_ == null ? com.google.protobuf.Value.getDefaultInstance() : value_;
  }
  /**
   *
   *
   * <pre>
   * The predicted value of the row's
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
   * The value depends on the column's DataType:
   * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
   *            value.
   * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
   * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
   *                   ARRAY in that column (with the above confidence `score`).
   * </pre>
   *
   * <code>.google.protobuf.Value value = 2;</code>
   */
  public com.google.protobuf.ValueOrBuilder getValueOrBuilder() {
    return getValue();
  }

  public static final int TABLES_MODEL_COLUMN_INFO_FIELD_NUMBER = 3;
  private java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>
      tablesModelColumnInfo_;
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the model's
   * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
   * with respect to this particular prediction.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
   * </code>
   */
  public java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>
      getTablesModelColumnInfoList() {
    return tablesModelColumnInfo_;
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the model's
   * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
   * with respect to this particular prediction.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
   * </code>
   */
  public java.util.List<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>
      getTablesModelColumnInfoOrBuilderList() {
    return tablesModelColumnInfo_;
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the model's
   * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
   * with respect to this particular prediction.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
   * </code>
   */
  public int getTablesModelColumnInfoCount() {
    return tablesModelColumnInfo_.size();
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the model's
   * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
   * with respect to this particular prediction.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
   * </code>
   */
  public com.google.cloud.automl.v1beta1.TablesModelColumnInfo getTablesModelColumnInfo(int index) {
    return tablesModelColumnInfo_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the model's
   * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
   * with respect to this particular prediction.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
   * </code>
   */
  public com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder
      getTablesModelColumnInfoOrBuilder(int index) {
    return tablesModelColumnInfo_.get(index);
  }

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  @java.lang.Override
  public void writeTo(com.google.protobuf.CodedOutputStream output) throws java.io.IOException {
    if (score_ != 0F) {
      output.writeFloat(1, score_);
    }
    if (value_ != null) {
      output.writeMessage(2, getValue());
    }
    for (int i = 0; i < tablesModelColumnInfo_.size(); i++) {
      output.writeMessage(3, tablesModelColumnInfo_.get(i));
    }
    if (predictionInterval_ != null) {
      output.writeMessage(4, getPredictionInterval());
    }
    unknownFields.writeTo(output);
  }

  @java.lang.Override
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    if (size != -1) return size;

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  @java.lang.Override
  public boolean equals(final java.lang.Object obj) {
    if (obj == this) {
      return true;
    }
    if (!(obj instanceof com.google.cloud.automl.v1beta1.TablesAnnotation)) {
      return super.equals(obj);
    }
    com.google.cloud.automl.v1beta1.TablesAnnotation other =
        (com.google.cloud.automl.v1beta1.TablesAnnotation) obj;

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        != java.lang.Float.floatToIntBits(other.getScore())) return false;
    if (hasPredictionInterval() != other.hasPredictionInterval()) return false;
    if (hasPredictionInterval()) {
      if (!getPredictionInterval().equals(other.getPredictionInterval())) return false;
    }
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    if (hasValue()) {
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    }
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  @java.lang.Override
  public int hashCode() {
    if (memoizedHashCode != 0) {
      return memoizedHashCode;
    }
    int hash = 41;
    hash = (19 * hash) + getDescriptor().hashCode();
    hash = (37 * hash) + SCORE_FIELD_NUMBER;
    hash = (53 * hash) + java.lang.Float.floatToIntBits(getScore());
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  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(java.nio.ByteBuffer data)
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  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
      java.nio.ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
      throws com.google.protobuf.InvalidProtocolBufferException {
    return PARSER.parseFrom(data, extensionRegistry);
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
      com.google.protobuf.ByteString data)
      throws com.google.protobuf.InvalidProtocolBufferException {
    return PARSER.parseFrom(data);
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
      com.google.protobuf.ByteString data,
      com.google.protobuf.ExtensionRegistryLite extensionRegistry)
      throws com.google.protobuf.InvalidProtocolBufferException {
    return PARSER.parseFrom(data, extensionRegistry);
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(byte[] data)
      throws com.google.protobuf.InvalidProtocolBufferException {
    return PARSER.parseFrom(data);
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
      byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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    return PARSER.parseFrom(data, extensionRegistry);
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
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    return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
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      throws java.io.IOException {
    return com.google.protobuf.GeneratedMessageV3.parseWithIOException(
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  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseDelimitedFrom(
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      throws java.io.IOException {
    return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(
        PARSER, input, extensionRegistry);
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  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
      com.google.protobuf.CodedInputStream input) throws java.io.IOException {
    return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation parseFrom(
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      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(com.google.cloud.automl.v1beta1.TablesAnnotation 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;
  }
  /**
   *
   *
   * <pre>
   * Contains annotation details specific to Tables.
   * </pre>
   *
   * Protobuf type {@code google.cloud.automl.v1beta1.TablesAnnotation}
   */
  public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder<Builder>
      implements
      // @@protoc_insertion_point(builder_implements:google.cloud.automl.v1beta1.TablesAnnotation)
      com.google.cloud.automl.v1beta1.TablesAnnotationOrBuilder {
    public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
      return com.google.cloud.automl.v1beta1.Tables
          .internal_static_google_cloud_automl_v1beta1_TablesAnnotation_descriptor;
    }

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    private Builder() {
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    private Builder(com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
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      maybeForceBuilderInitialization();
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    private void maybeForceBuilderInitialization() {
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      }
    }

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    public Builder clear() {
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      if (predictionIntervalBuilder_ == null) {
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      } else {
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        predictionIntervalBuilder_ = null;
      }
      if (valueBuilder_ == null) {
        value_ = null;
      } else {
        value_ = null;
        valueBuilder_ = null;
      }
      if (tablesModelColumnInfoBuilder_ == null) {
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        bitField0_ = (bitField0_ & ~0x00000008);
      } else {
        tablesModelColumnInfoBuilder_.clear();
      }
      return this;
    }

    @java.lang.Override
    public com.google.protobuf.Descriptors.Descriptor getDescriptorForType() {
      return com.google.cloud.automl.v1beta1.Tables
          .internal_static_google_cloud_automl_v1beta1_TablesAnnotation_descriptor;
    }

    @java.lang.Override
    public com.google.cloud.automl.v1beta1.TablesAnnotation getDefaultInstanceForType() {
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    public com.google.cloud.automl.v1beta1.TablesAnnotation build() {
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      if (!result.isInitialized()) {
        throw newUninitializedMessageException(result);
      }
      return result;
    }

    @java.lang.Override
    public com.google.cloud.automl.v1beta1.TablesAnnotation buildPartial() {
      com.google.cloud.automl.v1beta1.TablesAnnotation result =
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      int from_bitField0_ = bitField0_;
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      }
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          tablesModelColumnInfo_ = java.util.Collections.unmodifiableList(tablesModelColumnInfo_);
          bitField0_ = (bitField0_ & ~0x00000008);
        }
        result.tablesModelColumnInfo_ = tablesModelColumnInfo_;
      } else {
        result.tablesModelColumnInfo_ = tablesModelColumnInfoBuilder_.build();
      }
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        return this;
      }
    }

    public Builder mergeFrom(com.google.cloud.automl.v1beta1.TablesAnnotation other) {
      if (other == com.google.cloud.automl.v1beta1.TablesAnnotation.getDefaultInstance())
        return this;
      if (other.getScore() != 0F) {
        setScore(other.getScore());
      }
      if (other.hasPredictionInterval()) {
        mergePredictionInterval(other.getPredictionInterval());
      }
      if (other.hasValue()) {
        mergeValue(other.getValue());
      }
      if (tablesModelColumnInfoBuilder_ == null) {
        if (!other.tablesModelColumnInfo_.isEmpty()) {
          if (tablesModelColumnInfo_.isEmpty()) {
            tablesModelColumnInfo_ = other.tablesModelColumnInfo_;
            bitField0_ = (bitField0_ & ~0x00000008);
          } else {
            ensureTablesModelColumnInfoIsMutable();
            tablesModelColumnInfo_.addAll(other.tablesModelColumnInfo_);
          }
          onChanged();
        }
      } else {
        if (!other.tablesModelColumnInfo_.isEmpty()) {
          if (tablesModelColumnInfoBuilder_.isEmpty()) {
            tablesModelColumnInfoBuilder_.dispose();
            tablesModelColumnInfoBuilder_ = null;
            tablesModelColumnInfo_ = other.tablesModelColumnInfo_;
            bitField0_ = (bitField0_ & ~0x00000008);
            tablesModelColumnInfoBuilder_ =
                com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders
                    ? getTablesModelColumnInfoFieldBuilder()
                    : null;
          } else {
            tablesModelColumnInfoBuilder_.addAllMessages(other.tablesModelColumnInfo_);
          }
        }
      }
      this.mergeUnknownFields(other.unknownFields);
      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 {
      com.google.cloud.automl.v1beta1.TablesAnnotation parsedMessage = null;
      try {
        parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
      } catch (com.google.protobuf.InvalidProtocolBufferException e) {
        parsedMessage = (com.google.cloud.automl.v1beta1.TablesAnnotation) e.getUnfinishedMessage();
        throw e.unwrapIOException();
      } finally {
        if (parsedMessage != null) {
          mergeFrom(parsedMessage);
        }
      }
      return this;
    }

    private int bitField0_;

    private float score_;
    /**
     *
     *
     * <pre>
     * Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher
     * value means greater confidence in the returned value.
     * For
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * of ARRAY(CATEGORY) data type, this is a confidence that one of the values
     * in the ARRAY would be the provided value.
     * For
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * of FLOAT64 data type the score is not populated.
     * </pre>
     *
     * <code>float score = 1;</code>
     */
    public float getScore() {
      return score_;
    }
    /**
     *
     *
     * <pre>
     * Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher
     * value means greater confidence in the returned value.
     * For
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * of ARRAY(CATEGORY) data type, this is a confidence that one of the values
     * in the ARRAY would be the provided value.
     * For
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * of FLOAT64 data type the score is not populated.
     * </pre>
     *
     * <code>float score = 1;</code>
     */
    public Builder setScore(float value) {

      score_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. A confidence estimate between 0.0 and 1.0, inclusive. A higher
     * value means greater confidence in the returned value.
     * For
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * of ARRAY(CATEGORY) data type, this is a confidence that one of the values
     * in the ARRAY would be the provided value.
     * For
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * of FLOAT64 data type the score is not populated.
     * </pre>
     *
     * <code>float score = 1;</code>
     */
    public Builder clearScore() {

      score_ = 0F;
      onChanged();
      return this;
    }

    private com.google.cloud.automl.v1beta1.DoubleRange predictionInterval_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.DoubleRange,
            com.google.cloud.automl.v1beta1.DoubleRange.Builder,
            com.google.cloud.automl.v1beta1.DoubleRangeOrBuilder>
        predictionIntervalBuilder_;
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public boolean hasPredictionInterval() {
      return predictionIntervalBuilder_ != null || predictionInterval_ != null;
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public com.google.cloud.automl.v1beta1.DoubleRange getPredictionInterval() {
      if (predictionIntervalBuilder_ == null) {
        return predictionInterval_ == null
            ? com.google.cloud.automl.v1beta1.DoubleRange.getDefaultInstance()
            : predictionInterval_;
      } else {
        return predictionIntervalBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public Builder setPredictionInterval(com.google.cloud.automl.v1beta1.DoubleRange value) {
      if (predictionIntervalBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        predictionInterval_ = value;
        onChanged();
      } else {
        predictionIntervalBuilder_.setMessage(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public Builder setPredictionInterval(
        com.google.cloud.automl.v1beta1.DoubleRange.Builder builderForValue) {
      if (predictionIntervalBuilder_ == null) {
        predictionInterval_ = builderForValue.build();
        onChanged();
      } else {
        predictionIntervalBuilder_.setMessage(builderForValue.build());
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public Builder mergePredictionInterval(com.google.cloud.automl.v1beta1.DoubleRange value) {
      if (predictionIntervalBuilder_ == null) {
        if (predictionInterval_ != null) {
          predictionInterval_ =
              com.google.cloud.automl.v1beta1.DoubleRange.newBuilder(predictionInterval_)
                  .mergeFrom(value)
                  .buildPartial();
        } else {
          predictionInterval_ = value;
        }
        onChanged();
      } else {
        predictionIntervalBuilder_.mergeFrom(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public Builder clearPredictionInterval() {
      if (predictionIntervalBuilder_ == null) {
        predictionInterval_ = null;
        onChanged();
      } else {
        predictionInterval_ = null;
        predictionIntervalBuilder_ = null;
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public com.google.cloud.automl.v1beta1.DoubleRange.Builder getPredictionIntervalBuilder() {

      onChanged();
      return getPredictionIntervalFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    public com.google.cloud.automl.v1beta1.DoubleRangeOrBuilder getPredictionIntervalOrBuilder() {
      if (predictionIntervalBuilder_ != null) {
        return predictionIntervalBuilder_.getMessageOrBuilder();
      } else {
        return predictionInterval_ == null
            ? com.google.cloud.automl.v1beta1.DoubleRange.getDefaultInstance()
            : predictionInterval_;
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Only populated when
     * [target_column_spec][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * has FLOAT64 data type (i.e. for regression predictions). An interval in
     * which the exactly correct target value has 95% chance to be in.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.DoubleRange prediction_interval = 4;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.DoubleRange,
            com.google.cloud.automl.v1beta1.DoubleRange.Builder,
            com.google.cloud.automl.v1beta1.DoubleRangeOrBuilder>
        getPredictionIntervalFieldBuilder() {
      if (predictionIntervalBuilder_ == null) {
        predictionIntervalBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.cloud.automl.v1beta1.DoubleRange,
                com.google.cloud.automl.v1beta1.DoubleRange.Builder,
                com.google.cloud.automl.v1beta1.DoubleRangeOrBuilder>(
                getPredictionInterval(), getParentForChildren(), isClean());
        predictionInterval_ = null;
      }
      return predictionIntervalBuilder_;
    }

    private com.google.protobuf.Value value_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.protobuf.Value,
            com.google.protobuf.Value.Builder,
            com.google.protobuf.ValueOrBuilder>
        valueBuilder_;
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public boolean hasValue() {
      return valueBuilder_ != null || value_ != null;
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public com.google.protobuf.Value getValue() {
      if (valueBuilder_ == null) {
        return value_ == null ? com.google.protobuf.Value.getDefaultInstance() : value_;
      } else {
        return valueBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public Builder setValue(com.google.protobuf.Value value) {
      if (valueBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        value_ = value;
        onChanged();
      } else {
        valueBuilder_.setMessage(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public Builder setValue(com.google.protobuf.Value.Builder builderForValue) {
      if (valueBuilder_ == null) {
        value_ = builderForValue.build();
        onChanged();
      } else {
        valueBuilder_.setMessage(builderForValue.build());
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public Builder mergeValue(com.google.protobuf.Value value) {
      if (valueBuilder_ == null) {
        if (value_ != null) {
          value_ = com.google.protobuf.Value.newBuilder(value_).mergeFrom(value).buildPartial();
        } else {
          value_ = value;
        }
        onChanged();
      } else {
        valueBuilder_.mergeFrom(value);
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public Builder clearValue() {
      if (valueBuilder_ == null) {
        value_ = null;
        onChanged();
      } else {
        value_ = null;
        valueBuilder_ = null;
      }

      return this;
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public com.google.protobuf.Value.Builder getValueBuilder() {

      onChanged();
      return getValueFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    public com.google.protobuf.ValueOrBuilder getValueOrBuilder() {
      if (valueBuilder_ != null) {
        return valueBuilder_.getMessageOrBuilder();
      } else {
        return value_ == null ? com.google.protobuf.Value.getDefaultInstance() : value_;
      }
    }
    /**
     *
     *
     * <pre>
     * The predicted value of the row's
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec].
     * The value depends on the column's DataType:
     * CATEGORY - the predicted (with the above confidence `score`) CATEGORY
     *            value.
     * FLOAT64 - the predicted (with the above confidence `score`) FLOAT64 value.
     * ARRAY(CATEGORY) - CATEGORY value meaning that this value would be in the
     *                   ARRAY in that column (with the above confidence `score`).
     * </pre>
     *
     * <code>.google.protobuf.Value value = 2;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.protobuf.Value,
            com.google.protobuf.Value.Builder,
            com.google.protobuf.ValueOrBuilder>
        getValueFieldBuilder() {
      if (valueBuilder_ == null) {
        valueBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.protobuf.Value,
                com.google.protobuf.Value.Builder,
                com.google.protobuf.ValueOrBuilder>(getValue(), getParentForChildren(), isClean());
        value_ = null;
      }
      return valueBuilder_;
    }

    private java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>
        tablesModelColumnInfo_ = java.util.Collections.emptyList();

    private void ensureTablesModelColumnInfoIsMutable() {
      if (!((bitField0_ & 0x00000008) != 0)) {
        tablesModelColumnInfo_ =
            new java.util.ArrayList<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>(
                tablesModelColumnInfo_);
        bitField0_ |= 0x00000008;
      }
    }

    private com.google.protobuf.RepeatedFieldBuilderV3<
            com.google.cloud.automl.v1beta1.TablesModelColumnInfo,
            com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder,
            com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>
        tablesModelColumnInfoBuilder_;

    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>
        getTablesModelColumnInfoList() {
      if (tablesModelColumnInfoBuilder_ == null) {
        return java.util.Collections.unmodifiableList(tablesModelColumnInfo_);
      } else {
        return tablesModelColumnInfoBuilder_.getMessageList();
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public int getTablesModelColumnInfoCount() {
      if (tablesModelColumnInfoBuilder_ == null) {
        return tablesModelColumnInfo_.size();
      } else {
        return tablesModelColumnInfoBuilder_.getCount();
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public com.google.cloud.automl.v1beta1.TablesModelColumnInfo getTablesModelColumnInfo(
        int index) {
      if (tablesModelColumnInfoBuilder_ == null) {
        return tablesModelColumnInfo_.get(index);
      } else {
        return tablesModelColumnInfoBuilder_.getMessage(index);
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder setTablesModelColumnInfo(
        int index, com.google.cloud.automl.v1beta1.TablesModelColumnInfo value) {
      if (tablesModelColumnInfoBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureTablesModelColumnInfoIsMutable();
        tablesModelColumnInfo_.set(index, value);
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.setMessage(index, value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder setTablesModelColumnInfo(
        int index, com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder builderForValue) {
      if (tablesModelColumnInfoBuilder_ == null) {
        ensureTablesModelColumnInfoIsMutable();
        tablesModelColumnInfo_.set(index, builderForValue.build());
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.setMessage(index, builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder addTablesModelColumnInfo(
        com.google.cloud.automl.v1beta1.TablesModelColumnInfo value) {
      if (tablesModelColumnInfoBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureTablesModelColumnInfoIsMutable();
        tablesModelColumnInfo_.add(value);
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.addMessage(value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder addTablesModelColumnInfo(
        int index, com.google.cloud.automl.v1beta1.TablesModelColumnInfo value) {
      if (tablesModelColumnInfoBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureTablesModelColumnInfoIsMutable();
        tablesModelColumnInfo_.add(index, value);
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.addMessage(index, value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder addTablesModelColumnInfo(
        com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder builderForValue) {
      if (tablesModelColumnInfoBuilder_ == null) {
        ensureTablesModelColumnInfoIsMutable();
        tablesModelColumnInfo_.add(builderForValue.build());
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.addMessage(builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder addTablesModelColumnInfo(
        int index, com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder builderForValue) {
      if (tablesModelColumnInfoBuilder_ == null) {
        ensureTablesModelColumnInfoIsMutable();
        tablesModelColumnInfo_.add(index, builderForValue.build());
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.addMessage(index, builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder addAllTablesModelColumnInfo(
        java.lang.Iterable<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfo>
            values) {
      if (tablesModelColumnInfoBuilder_ == null) {
        ensureTablesModelColumnInfoIsMutable();
        com.google.protobuf.AbstractMessageLite.Builder.addAll(values, tablesModelColumnInfo_);
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.addAllMessages(values);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder clearTablesModelColumnInfo() {
      if (tablesModelColumnInfoBuilder_ == null) {
        tablesModelColumnInfo_ = java.util.Collections.emptyList();
        bitField0_ = (bitField0_ & ~0x00000008);
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public Builder removeTablesModelColumnInfo(int index) {
      if (tablesModelColumnInfoBuilder_ == null) {
        ensureTablesModelColumnInfoIsMutable();
        tablesModelColumnInfo_.remove(index);
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.remove(index);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder
        getTablesModelColumnInfoBuilder(int index) {
      return getTablesModelColumnInfoFieldBuilder().getBuilder(index);
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder
        getTablesModelColumnInfoOrBuilder(int index) {
      if (tablesModelColumnInfoBuilder_ == null) {
        return tablesModelColumnInfo_.get(index);
      } else {
        return tablesModelColumnInfoBuilder_.getMessageOrBuilder(index);
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public java.util.List<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>
        getTablesModelColumnInfoOrBuilderList() {
      if (tablesModelColumnInfoBuilder_ != null) {
        return tablesModelColumnInfoBuilder_.getMessageOrBuilderList();
      } else {
        return java.util.Collections.unmodifiableList(tablesModelColumnInfo_);
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder
        addTablesModelColumnInfoBuilder() {
      return getTablesModelColumnInfoFieldBuilder()
          .addBuilder(com.google.cloud.automl.v1beta1.TablesModelColumnInfo.getDefaultInstance());
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder
        addTablesModelColumnInfoBuilder(int index) {
      return getTablesModelColumnInfoFieldBuilder()
          .addBuilder(
              index, com.google.cloud.automl.v1beta1.TablesModelColumnInfo.getDefaultInstance());
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the model's
     * [input_feature_column_specs'][google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs]
     * with respect to this particular prediction.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 3;
     * </code>
     */
    public java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder>
        getTablesModelColumnInfoBuilderList() {
      return getTablesModelColumnInfoFieldBuilder().getBuilderList();
    }

    private com.google.protobuf.RepeatedFieldBuilderV3<
            com.google.cloud.automl.v1beta1.TablesModelColumnInfo,
            com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder,
            com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>
        getTablesModelColumnInfoFieldBuilder() {
      if (tablesModelColumnInfoBuilder_ == null) {
        tablesModelColumnInfoBuilder_ =
            new com.google.protobuf.RepeatedFieldBuilderV3<
                com.google.cloud.automl.v1beta1.TablesModelColumnInfo,
                com.google.cloud.automl.v1beta1.TablesModelColumnInfo.Builder,
                com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>(
                tablesModelColumnInfo_,
                ((bitField0_ & 0x00000008) != 0),
                getParentForChildren(),
                isClean());
        tablesModelColumnInfo_ = null;
      }
      return tablesModelColumnInfoBuilder_;
    }

    @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:google.cloud.automl.v1beta1.TablesAnnotation)
  }

  // @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesAnnotation)
  private static final com.google.cloud.automl.v1beta1.TablesAnnotation DEFAULT_INSTANCE;

  static {
    DEFAULT_INSTANCE = new com.google.cloud.automl.v1beta1.TablesAnnotation();
  }

  public static com.google.cloud.automl.v1beta1.TablesAnnotation getDefaultInstance() {
    return DEFAULT_INSTANCE;
  }

  private static final com.google.protobuf.Parser<TablesAnnotation> PARSER =
      new com.google.protobuf.AbstractParser<TablesAnnotation>() {
        @java.lang.Override
        public TablesAnnotation parsePartialFrom(
            com.google.protobuf.CodedInputStream input,
            com.google.protobuf.ExtensionRegistryLite extensionRegistry)
            throws com.google.protobuf.InvalidProtocolBufferException {
          return new TablesAnnotation(input, extensionRegistry);
        }
      };

  public static com.google.protobuf.Parser<TablesAnnotation> parser() {
    return PARSER;
  }

  @java.lang.Override
  public com.google.protobuf.Parser<TablesAnnotation> getParserForType() {
    return PARSER;
  }

  @java.lang.Override
  public com.google.cloud.automl.v1beta1.TablesAnnotation getDefaultInstanceForType() {
    return DEFAULT_INSTANCE;
  }
}
