/*
 * Copyright 2020 Google LLC
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     https://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: google/cloud/automl/v1beta1/tables.proto

package com.google.cloud.automl.v1beta1;

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

  private TablesModelMetadata() {
    inputFeatureColumnSpecs_ = java.util.Collections.emptyList();
    optimizationObjective_ = "";
    tablesModelColumnInfo_ = java.util.Collections.emptyList();
  }

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

  @java.lang.Override
  public final com.google.protobuf.UnknownFieldSet getUnknownFields() {
    return this.unknownFields;
  }

  public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
    return com.google.cloud.automl.v1beta1.Tables
        .internal_static_google_cloud_automl_v1beta1_TablesModelMetadata_descriptor;
  }

  @java.lang.Override
  protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
      internalGetFieldAccessorTable() {
    return com.google.cloud.automl.v1beta1.Tables
        .internal_static_google_cloud_automl_v1beta1_TablesModelMetadata_fieldAccessorTable
        .ensureFieldAccessorsInitialized(
            com.google.cloud.automl.v1beta1.TablesModelMetadata.class,
            com.google.cloud.automl.v1beta1.TablesModelMetadata.Builder.class);
  }

  private int additionalOptimizationObjectiveConfigCase_ = 0;
  private java.lang.Object additionalOptimizationObjectiveConfig_;

  public enum AdditionalOptimizationObjectiveConfigCase
      implements
          com.google.protobuf.Internal.EnumLite,
          com.google.protobuf.AbstractMessage.InternalOneOfEnum {
    OPTIMIZATION_OBJECTIVE_RECALL_VALUE(17),
    OPTIMIZATION_OBJECTIVE_PRECISION_VALUE(18),
    ADDITIONALOPTIMIZATIONOBJECTIVECONFIG_NOT_SET(0);
    private final int value;

    private AdditionalOptimizationObjectiveConfigCase(int value) {
      this.value = value;
    }
    /**
     * @param value The number of the enum to look for.
     * @return The enum associated with the given number.
     * @deprecated Use {@link #forNumber(int)} instead.
     */
    @java.lang.Deprecated
    public static AdditionalOptimizationObjectiveConfigCase valueOf(int value) {
      return forNumber(value);
    }

    public static AdditionalOptimizationObjectiveConfigCase forNumber(int value) {
      switch (value) {
        case 17:
          return OPTIMIZATION_OBJECTIVE_RECALL_VALUE;
        case 18:
          return OPTIMIZATION_OBJECTIVE_PRECISION_VALUE;
        case 0:
          return ADDITIONALOPTIMIZATIONOBJECTIVECONFIG_NOT_SET;
        default:
          return null;
      }
    }

    public int getNumber() {
      return this.value;
    }
  };

  public AdditionalOptimizationObjectiveConfigCase getAdditionalOptimizationObjectiveConfigCase() {
    return AdditionalOptimizationObjectiveConfigCase.forNumber(
        additionalOptimizationObjectiveConfigCase_);
  }

  public static final int OPTIMIZATION_OBJECTIVE_RECALL_VALUE_FIELD_NUMBER = 17;
  /**
   *
   *
   * <pre>
   * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
   * Must be between 0 and 1, inclusive.
   * </pre>
   *
   * <code>float optimization_objective_recall_value = 17;</code>
   *
   * @return Whether the optimizationObjectiveRecallValue field is set.
   */
  @java.lang.Override
  public boolean hasOptimizationObjectiveRecallValue() {
    return additionalOptimizationObjectiveConfigCase_ == 17;
  }
  /**
   *
   *
   * <pre>
   * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
   * Must be between 0 and 1, inclusive.
   * </pre>
   *
   * <code>float optimization_objective_recall_value = 17;</code>
   *
   * @return The optimizationObjectiveRecallValue.
   */
  @java.lang.Override
  public float getOptimizationObjectiveRecallValue() {
    if (additionalOptimizationObjectiveConfigCase_ == 17) {
      return (java.lang.Float) additionalOptimizationObjectiveConfig_;
    }
    return 0F;
  }

  public static final int OPTIMIZATION_OBJECTIVE_PRECISION_VALUE_FIELD_NUMBER = 18;
  /**
   *
   *
   * <pre>
   * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
   * Must be between 0 and 1, inclusive.
   * </pre>
   *
   * <code>float optimization_objective_precision_value = 18;</code>
   *
   * @return Whether the optimizationObjectivePrecisionValue field is set.
   */
  @java.lang.Override
  public boolean hasOptimizationObjectivePrecisionValue() {
    return additionalOptimizationObjectiveConfigCase_ == 18;
  }
  /**
   *
   *
   * <pre>
   * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
   * Must be between 0 and 1, inclusive.
   * </pre>
   *
   * <code>float optimization_objective_precision_value = 18;</code>
   *
   * @return The optimizationObjectivePrecisionValue.
   */
  @java.lang.Override
  public float getOptimizationObjectivePrecisionValue() {
    if (additionalOptimizationObjectiveConfigCase_ == 18) {
      return (java.lang.Float) additionalOptimizationObjectiveConfig_;
    }
    return 0F;
  }

  public static final int TARGET_COLUMN_SPEC_FIELD_NUMBER = 2;
  private com.google.cloud.automl.v1beta1.ColumnSpec targetColumnSpec_;
  /**
   *
   *
   * <pre>
   * Column spec of the dataset's primary table's column the model is
   * predicting. Snapshotted when model creation started.
   * Only 3 fields are used:
   * name - May be set on CreateModel, if it's not then the ColumnSpec
   *        corresponding to the current target_column_spec_id of the dataset
   *        the model is trained from is used.
   *        If neither is set, CreateModel will error.
   * display_name - Output only.
   * data_type - Output only.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
   *
   * @return Whether the targetColumnSpec field is set.
   */
  @java.lang.Override
  public boolean hasTargetColumnSpec() {
    return targetColumnSpec_ != null;
  }
  /**
   *
   *
   * <pre>
   * Column spec of the dataset's primary table's column the model is
   * predicting. Snapshotted when model creation started.
   * Only 3 fields are used:
   * name - May be set on CreateModel, if it's not then the ColumnSpec
   *        corresponding to the current target_column_spec_id of the dataset
   *        the model is trained from is used.
   *        If neither is set, CreateModel will error.
   * display_name - Output only.
   * data_type - Output only.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
   *
   * @return The targetColumnSpec.
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.ColumnSpec getTargetColumnSpec() {
    return targetColumnSpec_ == null
        ? com.google.cloud.automl.v1beta1.ColumnSpec.getDefaultInstance()
        : targetColumnSpec_;
  }
  /**
   *
   *
   * <pre>
   * Column spec of the dataset's primary table's column the model is
   * predicting. Snapshotted when model creation started.
   * Only 3 fields are used:
   * name - May be set on CreateModel, if it's not then the ColumnSpec
   *        corresponding to the current target_column_spec_id of the dataset
   *        the model is trained from is used.
   *        If neither is set, CreateModel will error.
   * display_name - Output only.
   * data_type - Output only.
   * </pre>
   *
   * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getTargetColumnSpecOrBuilder() {
    return targetColumnSpec_ == null
        ? com.google.cloud.automl.v1beta1.ColumnSpec.getDefaultInstance()
        : targetColumnSpec_;
  }

  public static final int INPUT_FEATURE_COLUMN_SPECS_FIELD_NUMBER = 3;

  @SuppressWarnings("serial")
  private java.util.List<com.google.cloud.automl.v1beta1.ColumnSpec> inputFeatureColumnSpecs_;
  /**
   *
   *
   * <pre>
   * Column specs of the dataset's primary table's columns, on which
   * the model is trained and which are used as the input for predictions.
   * The
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * as well as, according to dataset's state upon model creation,
   * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
   * and
   * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
   * must never be included here.
   * Only 3 fields are used:
   * * name - May be set on CreateModel, if set only the columns specified are
   *   used, otherwise all primary table's columns (except the ones listed
   *   above) are used for the training and prediction input.
   * * display_name - Output only.
   * * data_type - Output only.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
   */
  @java.lang.Override
  public java.util.List<com.google.cloud.automl.v1beta1.ColumnSpec>
      getInputFeatureColumnSpecsList() {
    return inputFeatureColumnSpecs_;
  }
  /**
   *
   *
   * <pre>
   * Column specs of the dataset's primary table's columns, on which
   * the model is trained and which are used as the input for predictions.
   * The
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * as well as, according to dataset's state upon model creation,
   * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
   * and
   * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
   * must never be included here.
   * Only 3 fields are used:
   * * name - May be set on CreateModel, if set only the columns specified are
   *   used, otherwise all primary table's columns (except the ones listed
   *   above) are used for the training and prediction input.
   * * display_name - Output only.
   * * data_type - Output only.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
   */
  @java.lang.Override
  public java.util.List<? extends com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>
      getInputFeatureColumnSpecsOrBuilderList() {
    return inputFeatureColumnSpecs_;
  }
  /**
   *
   *
   * <pre>
   * Column specs of the dataset's primary table's columns, on which
   * the model is trained and which are used as the input for predictions.
   * The
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * as well as, according to dataset's state upon model creation,
   * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
   * and
   * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
   * must never be included here.
   * Only 3 fields are used:
   * * name - May be set on CreateModel, if set only the columns specified are
   *   used, otherwise all primary table's columns (except the ones listed
   *   above) are used for the training and prediction input.
   * * display_name - Output only.
   * * data_type - Output only.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
   */
  @java.lang.Override
  public int getInputFeatureColumnSpecsCount() {
    return inputFeatureColumnSpecs_.size();
  }
  /**
   *
   *
   * <pre>
   * Column specs of the dataset's primary table's columns, on which
   * the model is trained and which are used as the input for predictions.
   * The
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * as well as, according to dataset's state upon model creation,
   * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
   * and
   * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
   * must never be included here.
   * Only 3 fields are used:
   * * name - May be set on CreateModel, if set only the columns specified are
   *   used, otherwise all primary table's columns (except the ones listed
   *   above) are used for the training and prediction input.
   * * display_name - Output only.
   * * data_type - Output only.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.ColumnSpec getInputFeatureColumnSpecs(int index) {
    return inputFeatureColumnSpecs_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Column specs of the dataset's primary table's columns, on which
   * the model is trained and which are used as the input for predictions.
   * The
   * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
   * as well as, according to dataset's state upon model creation,
   * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
   * and
   * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
   * must never be included here.
   * Only 3 fields are used:
   * * name - May be set on CreateModel, if set only the columns specified are
   *   used, otherwise all primary table's columns (except the ones listed
   *   above) are used for the training and prediction input.
   * * display_name - Output only.
   * * data_type - Output only.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getInputFeatureColumnSpecsOrBuilder(
      int index) {
    return inputFeatureColumnSpecs_.get(index);
  }

  public static final int OPTIMIZATION_OBJECTIVE_FIELD_NUMBER = 4;

  @SuppressWarnings("serial")
  private volatile java.lang.Object optimizationObjective_ = "";
  /**
   *
   *
   * <pre>
   * Objective function the model is optimizing towards. The training process
   * creates a model that maximizes/minimizes the value of the objective
   * function over the validation set.
   * The supported optimization objectives depend on the prediction type.
   * If the field is not set, a default objective function is used.
   * CLASSIFICATION_BINARY:
   *   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
   *                                 operating characteristic (ROC) curve.
   *   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   *   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   *   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
   *                                   recall value.
   *   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
   *                                    precision value.
   * CLASSIFICATION_MULTI_CLASS :
   *   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
   * REGRESSION:
   *   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   *   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   *   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
   * </pre>
   *
   * <code>string optimization_objective = 4;</code>
   *
   * @return The optimizationObjective.
   */
  @java.lang.Override
  public java.lang.String getOptimizationObjective() {
    java.lang.Object ref = optimizationObjective_;
    if (ref instanceof java.lang.String) {
      return (java.lang.String) ref;
    } else {
      com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
      java.lang.String s = bs.toStringUtf8();
      optimizationObjective_ = s;
      return s;
    }
  }
  /**
   *
   *
   * <pre>
   * Objective function the model is optimizing towards. The training process
   * creates a model that maximizes/minimizes the value of the objective
   * function over the validation set.
   * The supported optimization objectives depend on the prediction type.
   * If the field is not set, a default objective function is used.
   * CLASSIFICATION_BINARY:
   *   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
   *                                 operating characteristic (ROC) curve.
   *   "MINIMIZE_LOG_LOSS" - Minimize log loss.
   *   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
   *   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
   *                                   recall value.
   *   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
   *                                    precision value.
   * CLASSIFICATION_MULTI_CLASS :
   *   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
   * REGRESSION:
   *   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
   *   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
   *   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
   * </pre>
   *
   * <code>string optimization_objective = 4;</code>
   *
   * @return The bytes for optimizationObjective.
   */
  @java.lang.Override
  public com.google.protobuf.ByteString getOptimizationObjectiveBytes() {
    java.lang.Object ref = optimizationObjective_;
    if (ref instanceof java.lang.String) {
      com.google.protobuf.ByteString b =
          com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
      optimizationObjective_ = b;
      return b;
    } else {
      return (com.google.protobuf.ByteString) ref;
    }
  }

  public static final int TABLES_MODEL_COLUMN_INFO_FIELD_NUMBER = 5;

  @SuppressWarnings("serial")
  private java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>
      tablesModelColumnInfo_;
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the
   * input_feature_column_specs with respect to this particular model.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
   * </code>
   */
  @java.lang.Override
  public java.util.List<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>
      getTablesModelColumnInfoList() {
    return tablesModelColumnInfo_;
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the
   * input_feature_column_specs with respect to this particular model.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
   * </code>
   */
  @java.lang.Override
  public java.util.List<? extends com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder>
      getTablesModelColumnInfoOrBuilderList() {
    return tablesModelColumnInfo_;
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the
   * input_feature_column_specs with respect to this particular model.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
   * </code>
   */
  @java.lang.Override
  public int getTablesModelColumnInfoCount() {
    return tablesModelColumnInfo_.size();
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the
   * input_feature_column_specs with respect to this particular model.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
   * </code>
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.TablesModelColumnInfo getTablesModelColumnInfo(int index) {
    return tablesModelColumnInfo_.get(index);
  }
  /**
   *
   *
   * <pre>
   * Output only. Auxiliary information for each of the
   * input_feature_column_specs with respect to this particular model.
   * </pre>
   *
   * <code>repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
   * </code>
   */
  @java.lang.Override
  public com.google.cloud.automl.v1beta1.TablesModelColumnInfoOrBuilder
      getTablesModelColumnInfoOrBuilder(int index) {
    return tablesModelColumnInfo_.get(index);
  }

  public static final int TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER = 6;
  private long trainBudgetMilliNodeHours_ = 0L;
  /**
   *
   *
   * <pre>
   * Required. The train budget of creating this model, expressed in milli node
   * hours i.e. 1,000 value in this field means 1 node hour.
   * The training cost of the model will not exceed this budget. The final cost
   * will be attempted to be close to the budget, though may end up being (even)
   * noticeably smaller - at the backend's discretion. This especially may
   * happen when further model training ceases to provide any improvements.
   * If the budget is set to a value known to be insufficient to train a
   * model for the given dataset, the training won't be attempted and
   * will error.
   * The train budget must be between 1,000 and 72,000 milli node hours,
   * inclusive.
   * </pre>
   *
   * <code>int64 train_budget_milli_node_hours = 6;</code>
   *
   * @return The trainBudgetMilliNodeHours.
   */
  @java.lang.Override
  public long getTrainBudgetMilliNodeHours() {
    return trainBudgetMilliNodeHours_;
  }

  public static final int TRAIN_COST_MILLI_NODE_HOURS_FIELD_NUMBER = 7;
  private long trainCostMilliNodeHours_ = 0L;
  /**
   *
   *
   * <pre>
   * Output only. The actual training cost of the model, expressed in milli
   * node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
   * to not exceed the train budget.
   * </pre>
   *
   * <code>int64 train_cost_milli_node_hours = 7;</code>
   *
   * @return The trainCostMilliNodeHours.
   */
  @java.lang.Override
  public long getTrainCostMilliNodeHours() {
    return trainCostMilliNodeHours_;
  }

  public static final int DISABLE_EARLY_STOPPING_FIELD_NUMBER = 12;
  private boolean disableEarlyStopping_ = false;
  /**
   *
   *
   * <pre>
   * Use the entire training budget. This disables the early stopping feature.
   * By default, the early stopping feature is enabled, which means that AutoML
   * Tables might stop training before the entire training budget has been used.
   * </pre>
   *
   * <code>bool disable_early_stopping = 12;</code>
   *
   * @return The disableEarlyStopping.
   */
  @java.lang.Override
  public boolean getDisableEarlyStopping() {
    return disableEarlyStopping_;
  }

  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 (targetColumnSpec_ != null) {
      output.writeMessage(2, getTargetColumnSpec());
    }
    for (int i = 0; i < inputFeatureColumnSpecs_.size(); i++) {
      output.writeMessage(3, inputFeatureColumnSpecs_.get(i));
    }
    if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(optimizationObjective_)) {
      com.google.protobuf.GeneratedMessageV3.writeString(output, 4, optimizationObjective_);
    }
    for (int i = 0; i < tablesModelColumnInfo_.size(); i++) {
      output.writeMessage(5, tablesModelColumnInfo_.get(i));
    }
    if (trainBudgetMilliNodeHours_ != 0L) {
      output.writeInt64(6, trainBudgetMilliNodeHours_);
    }
    if (trainCostMilliNodeHours_ != 0L) {
      output.writeInt64(7, trainCostMilliNodeHours_);
    }
    if (disableEarlyStopping_ != false) {
      output.writeBool(12, disableEarlyStopping_);
    }
    if (additionalOptimizationObjectiveConfigCase_ == 17) {
      output.writeFloat(17, (float) ((java.lang.Float) additionalOptimizationObjectiveConfig_));
    }
    if (additionalOptimizationObjectiveConfigCase_ == 18) {
      output.writeFloat(18, (float) ((java.lang.Float) additionalOptimizationObjectiveConfig_));
    }
    getUnknownFields().writeTo(output);
  }

  @java.lang.Override
  public int getSerializedSize() {
    int size = memoizedSize;
    if (size != -1) return size;

    size = 0;
    if (targetColumnSpec_ != null) {
      size += com.google.protobuf.CodedOutputStream.computeMessageSize(2, getTargetColumnSpec());
    }
    for (int i = 0; i < inputFeatureColumnSpecs_.size(); i++) {
      size +=
          com.google.protobuf.CodedOutputStream.computeMessageSize(
              3, inputFeatureColumnSpecs_.get(i));
    }
    if (!com.google.protobuf.GeneratedMessageV3.isStringEmpty(optimizationObjective_)) {
      size += com.google.protobuf.GeneratedMessageV3.computeStringSize(4, optimizationObjective_);
    }
    for (int i = 0; i < tablesModelColumnInfo_.size(); i++) {
      size +=
          com.google.protobuf.CodedOutputStream.computeMessageSize(
              5, tablesModelColumnInfo_.get(i));
    }
    if (trainBudgetMilliNodeHours_ != 0L) {
      size += com.google.protobuf.CodedOutputStream.computeInt64Size(6, trainBudgetMilliNodeHours_);
    }
    if (trainCostMilliNodeHours_ != 0L) {
      size += com.google.protobuf.CodedOutputStream.computeInt64Size(7, trainCostMilliNodeHours_);
    }
    if (disableEarlyStopping_ != false) {
      size += com.google.protobuf.CodedOutputStream.computeBoolSize(12, disableEarlyStopping_);
    }
    if (additionalOptimizationObjectiveConfigCase_ == 17) {
      size +=
          com.google.protobuf.CodedOutputStream.computeFloatSize(
              17, (float) ((java.lang.Float) additionalOptimizationObjectiveConfig_));
    }
    if (additionalOptimizationObjectiveConfigCase_ == 18) {
      size +=
          com.google.protobuf.CodedOutputStream.computeFloatSize(
              18, (float) ((java.lang.Float) additionalOptimizationObjectiveConfig_));
    }
    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 com.google.cloud.automl.v1beta1.TablesModelMetadata)) {
      return super.equals(obj);
    }
    com.google.cloud.automl.v1beta1.TablesModelMetadata other =
        (com.google.cloud.automl.v1beta1.TablesModelMetadata) obj;

    if (hasTargetColumnSpec() != other.hasTargetColumnSpec()) return false;
    if (hasTargetColumnSpec()) {
      if (!getTargetColumnSpec().equals(other.getTargetColumnSpec())) return false;
    }
    if (!getInputFeatureColumnSpecsList().equals(other.getInputFeatureColumnSpecsList()))
      return false;
    if (!getOptimizationObjective().equals(other.getOptimizationObjective())) return false;
    if (!getTablesModelColumnInfoList().equals(other.getTablesModelColumnInfoList())) return false;
    if (getTrainBudgetMilliNodeHours() != other.getTrainBudgetMilliNodeHours()) return false;
    if (getTrainCostMilliNodeHours() != other.getTrainCostMilliNodeHours()) return false;
    if (getDisableEarlyStopping() != other.getDisableEarlyStopping()) return false;
    if (!getAdditionalOptimizationObjectiveConfigCase()
        .equals(other.getAdditionalOptimizationObjectiveConfigCase())) return false;
    switch (additionalOptimizationObjectiveConfigCase_) {
      case 17:
        if (java.lang.Float.floatToIntBits(getOptimizationObjectiveRecallValue())
            != java.lang.Float.floatToIntBits(other.getOptimizationObjectiveRecallValue()))
          return false;
        break;
      case 18:
        if (java.lang.Float.floatToIntBits(getOptimizationObjectivePrecisionValue())
            != java.lang.Float.floatToIntBits(other.getOptimizationObjectivePrecisionValue()))
          return false;
        break;
      case 0:
      default:
    }
    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 (hasTargetColumnSpec()) {
      hash = (37 * hash) + TARGET_COLUMN_SPEC_FIELD_NUMBER;
      hash = (53 * hash) + getTargetColumnSpec().hashCode();
    }
    if (getInputFeatureColumnSpecsCount() > 0) {
      hash = (37 * hash) + INPUT_FEATURE_COLUMN_SPECS_FIELD_NUMBER;
      hash = (53 * hash) + getInputFeatureColumnSpecsList().hashCode();
    }
    hash = (37 * hash) + OPTIMIZATION_OBJECTIVE_FIELD_NUMBER;
    hash = (53 * hash) + getOptimizationObjective().hashCode();
    if (getTablesModelColumnInfoCount() > 0) {
      hash = (37 * hash) + TABLES_MODEL_COLUMN_INFO_FIELD_NUMBER;
      hash = (53 * hash) + getTablesModelColumnInfoList().hashCode();
    }
    hash = (37 * hash) + TRAIN_BUDGET_MILLI_NODE_HOURS_FIELD_NUMBER;
    hash = (53 * hash) + com.google.protobuf.Internal.hashLong(getTrainBudgetMilliNodeHours());
    hash = (37 * hash) + TRAIN_COST_MILLI_NODE_HOURS_FIELD_NUMBER;
    hash = (53 * hash) + com.google.protobuf.Internal.hashLong(getTrainCostMilliNodeHours());
    hash = (37 * hash) + DISABLE_EARLY_STOPPING_FIELD_NUMBER;
    hash = (53 * hash) + com.google.protobuf.Internal.hashBoolean(getDisableEarlyStopping());
    switch (additionalOptimizationObjectiveConfigCase_) {
      case 17:
        hash = (37 * hash) + OPTIMIZATION_OBJECTIVE_RECALL_VALUE_FIELD_NUMBER;
        hash = (53 * hash) + java.lang.Float.floatToIntBits(getOptimizationObjectiveRecallValue());
        break;
      case 18:
        hash = (37 * hash) + OPTIMIZATION_OBJECTIVE_PRECISION_VALUE_FIELD_NUMBER;
        hash =
            (53 * hash) + java.lang.Float.floatToIntBits(getOptimizationObjectivePrecisionValue());
        break;
      case 0:
      default:
    }
    hash = (29 * hash) + getUnknownFields().hashCode();
    memoizedHashCode = hash;
    return hash;
  }

  public static com.google.cloud.automl.v1beta1.TablesModelMetadata parseFrom(
      java.nio.ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException {
    return PARSER.parseFrom(data);
  }

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

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

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

  public static com.google.cloud.automl.v1beta1.TablesModelMetadata parseFrom(
      java.io.InputStream input) throws java.io.IOException {
    return com.google.protobuf.GeneratedMessageV3.parseWithIOException(PARSER, input);
  }

  public static com.google.cloud.automl.v1beta1.TablesModelMetadata 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 com.google.cloud.automl.v1beta1.TablesModelMetadata parseDelimitedFrom(
      java.io.InputStream input) throws java.io.IOException {
    return com.google.protobuf.GeneratedMessageV3.parseDelimitedWithIOException(PARSER, input);
  }

  public static com.google.cloud.automl.v1beta1.TablesModelMetadata 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 com.google.cloud.automl.v1beta1.TablesModelMetadata 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.TablesModelMetadata 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(com.google.cloud.automl.v1beta1.TablesModelMetadata 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>
   * Model metadata specific to AutoML Tables.
   * </pre>
   *
   * Protobuf type {@code google.cloud.automl.v1beta1.TablesModelMetadata}
   */
  public static final class Builder extends com.google.protobuf.GeneratedMessageV3.Builder<Builder>
      implements
      // @@protoc_insertion_point(builder_implements:google.cloud.automl.v1beta1.TablesModelMetadata)
      com.google.cloud.automl.v1beta1.TablesModelMetadataOrBuilder {
    public static final com.google.protobuf.Descriptors.Descriptor getDescriptor() {
      return com.google.cloud.automl.v1beta1.Tables
          .internal_static_google_cloud_automl_v1beta1_TablesModelMetadata_descriptor;
    }

    @java.lang.Override
    protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
        internalGetFieldAccessorTable() {
      return com.google.cloud.automl.v1beta1.Tables
          .internal_static_google_cloud_automl_v1beta1_TablesModelMetadata_fieldAccessorTable
          .ensureFieldAccessorsInitialized(
              com.google.cloud.automl.v1beta1.TablesModelMetadata.class,
              com.google.cloud.automl.v1beta1.TablesModelMetadata.Builder.class);
    }

    // Construct using com.google.cloud.automl.v1beta1.TablesModelMetadata.newBuilder()
    private Builder() {}

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

    @java.lang.Override
    public Builder clear() {
      super.clear();
      bitField0_ = 0;
      targetColumnSpec_ = null;
      if (targetColumnSpecBuilder_ != null) {
        targetColumnSpecBuilder_.dispose();
        targetColumnSpecBuilder_ = null;
      }
      if (inputFeatureColumnSpecsBuilder_ == null) {
        inputFeatureColumnSpecs_ = java.util.Collections.emptyList();
      } else {
        inputFeatureColumnSpecs_ = null;
        inputFeatureColumnSpecsBuilder_.clear();
      }
      bitField0_ = (bitField0_ & ~0x00000008);
      optimizationObjective_ = "";
      if (tablesModelColumnInfoBuilder_ == null) {
        tablesModelColumnInfo_ = java.util.Collections.emptyList();
      } else {
        tablesModelColumnInfo_ = null;
        tablesModelColumnInfoBuilder_.clear();
      }
      bitField0_ = (bitField0_ & ~0x00000020);
      trainBudgetMilliNodeHours_ = 0L;
      trainCostMilliNodeHours_ = 0L;
      disableEarlyStopping_ = false;
      additionalOptimizationObjectiveConfigCase_ = 0;
      additionalOptimizationObjectiveConfig_ = null;
      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_TablesModelMetadata_descriptor;
    }

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

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

    @java.lang.Override
    public com.google.cloud.automl.v1beta1.TablesModelMetadata buildPartial() {
      com.google.cloud.automl.v1beta1.TablesModelMetadata result =
          new com.google.cloud.automl.v1beta1.TablesModelMetadata(this);
      buildPartialRepeatedFields(result);
      if (bitField0_ != 0) {
        buildPartial0(result);
      }
      buildPartialOneofs(result);
      onBuilt();
      return result;
    }

    private void buildPartialRepeatedFields(
        com.google.cloud.automl.v1beta1.TablesModelMetadata result) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        if (((bitField0_ & 0x00000008) != 0)) {
          inputFeatureColumnSpecs_ =
              java.util.Collections.unmodifiableList(inputFeatureColumnSpecs_);
          bitField0_ = (bitField0_ & ~0x00000008);
        }
        result.inputFeatureColumnSpecs_ = inputFeatureColumnSpecs_;
      } else {
        result.inputFeatureColumnSpecs_ = inputFeatureColumnSpecsBuilder_.build();
      }
      if (tablesModelColumnInfoBuilder_ == null) {
        if (((bitField0_ & 0x00000020) != 0)) {
          tablesModelColumnInfo_ = java.util.Collections.unmodifiableList(tablesModelColumnInfo_);
          bitField0_ = (bitField0_ & ~0x00000020);
        }
        result.tablesModelColumnInfo_ = tablesModelColumnInfo_;
      } else {
        result.tablesModelColumnInfo_ = tablesModelColumnInfoBuilder_.build();
      }
    }

    private void buildPartial0(com.google.cloud.automl.v1beta1.TablesModelMetadata result) {
      int from_bitField0_ = bitField0_;
      if (((from_bitField0_ & 0x00000004) != 0)) {
        result.targetColumnSpec_ =
            targetColumnSpecBuilder_ == null ? targetColumnSpec_ : targetColumnSpecBuilder_.build();
      }
      if (((from_bitField0_ & 0x00000010) != 0)) {
        result.optimizationObjective_ = optimizationObjective_;
      }
      if (((from_bitField0_ & 0x00000040) != 0)) {
        result.trainBudgetMilliNodeHours_ = trainBudgetMilliNodeHours_;
      }
      if (((from_bitField0_ & 0x00000080) != 0)) {
        result.trainCostMilliNodeHours_ = trainCostMilliNodeHours_;
      }
      if (((from_bitField0_ & 0x00000100) != 0)) {
        result.disableEarlyStopping_ = disableEarlyStopping_;
      }
    }

    private void buildPartialOneofs(com.google.cloud.automl.v1beta1.TablesModelMetadata result) {
      result.additionalOptimizationObjectiveConfigCase_ =
          additionalOptimizationObjectiveConfigCase_;
      result.additionalOptimizationObjectiveConfig_ = this.additionalOptimizationObjectiveConfig_;
    }

    @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 com.google.cloud.automl.v1beta1.TablesModelMetadata) {
        return mergeFrom((com.google.cloud.automl.v1beta1.TablesModelMetadata) other);
      } else {
        super.mergeFrom(other);
        return this;
      }
    }

    public Builder mergeFrom(com.google.cloud.automl.v1beta1.TablesModelMetadata other) {
      if (other == com.google.cloud.automl.v1beta1.TablesModelMetadata.getDefaultInstance())
        return this;
      if (other.hasTargetColumnSpec()) {
        mergeTargetColumnSpec(other.getTargetColumnSpec());
      }
      if (inputFeatureColumnSpecsBuilder_ == null) {
        if (!other.inputFeatureColumnSpecs_.isEmpty()) {
          if (inputFeatureColumnSpecs_.isEmpty()) {
            inputFeatureColumnSpecs_ = other.inputFeatureColumnSpecs_;
            bitField0_ = (bitField0_ & ~0x00000008);
          } else {
            ensureInputFeatureColumnSpecsIsMutable();
            inputFeatureColumnSpecs_.addAll(other.inputFeatureColumnSpecs_);
          }
          onChanged();
        }
      } else {
        if (!other.inputFeatureColumnSpecs_.isEmpty()) {
          if (inputFeatureColumnSpecsBuilder_.isEmpty()) {
            inputFeatureColumnSpecsBuilder_.dispose();
            inputFeatureColumnSpecsBuilder_ = null;
            inputFeatureColumnSpecs_ = other.inputFeatureColumnSpecs_;
            bitField0_ = (bitField0_ & ~0x00000008);
            inputFeatureColumnSpecsBuilder_ =
                com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders
                    ? getInputFeatureColumnSpecsFieldBuilder()
                    : null;
          } else {
            inputFeatureColumnSpecsBuilder_.addAllMessages(other.inputFeatureColumnSpecs_);
          }
        }
      }
      if (!other.getOptimizationObjective().isEmpty()) {
        optimizationObjective_ = other.optimizationObjective_;
        bitField0_ |= 0x00000010;
        onChanged();
      }
      if (tablesModelColumnInfoBuilder_ == null) {
        if (!other.tablesModelColumnInfo_.isEmpty()) {
          if (tablesModelColumnInfo_.isEmpty()) {
            tablesModelColumnInfo_ = other.tablesModelColumnInfo_;
            bitField0_ = (bitField0_ & ~0x00000020);
          } else {
            ensureTablesModelColumnInfoIsMutable();
            tablesModelColumnInfo_.addAll(other.tablesModelColumnInfo_);
          }
          onChanged();
        }
      } else {
        if (!other.tablesModelColumnInfo_.isEmpty()) {
          if (tablesModelColumnInfoBuilder_.isEmpty()) {
            tablesModelColumnInfoBuilder_.dispose();
            tablesModelColumnInfoBuilder_ = null;
            tablesModelColumnInfo_ = other.tablesModelColumnInfo_;
            bitField0_ = (bitField0_ & ~0x00000020);
            tablesModelColumnInfoBuilder_ =
                com.google.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders
                    ? getTablesModelColumnInfoFieldBuilder()
                    : null;
          } else {
            tablesModelColumnInfoBuilder_.addAllMessages(other.tablesModelColumnInfo_);
          }
        }
      }
      if (other.getTrainBudgetMilliNodeHours() != 0L) {
        setTrainBudgetMilliNodeHours(other.getTrainBudgetMilliNodeHours());
      }
      if (other.getTrainCostMilliNodeHours() != 0L) {
        setTrainCostMilliNodeHours(other.getTrainCostMilliNodeHours());
      }
      if (other.getDisableEarlyStopping() != false) {
        setDisableEarlyStopping(other.getDisableEarlyStopping());
      }
      switch (other.getAdditionalOptimizationObjectiveConfigCase()) {
        case OPTIMIZATION_OBJECTIVE_RECALL_VALUE:
          {
            setOptimizationObjectiveRecallValue(other.getOptimizationObjectiveRecallValue());
            break;
          }
        case OPTIMIZATION_OBJECTIVE_PRECISION_VALUE:
          {
            setOptimizationObjectivePrecisionValue(other.getOptimizationObjectivePrecisionValue());
            break;
          }
        case ADDITIONALOPTIMIZATIONOBJECTIVECONFIG_NOT_SET:
          {
            break;
          }
      }
      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 18:
              {
                input.readMessage(
                    getTargetColumnSpecFieldBuilder().getBuilder(), extensionRegistry);
                bitField0_ |= 0x00000004;
                break;
              } // case 18
            case 26:
              {
                com.google.cloud.automl.v1beta1.ColumnSpec m =
                    input.readMessage(
                        com.google.cloud.automl.v1beta1.ColumnSpec.parser(), extensionRegistry);
                if (inputFeatureColumnSpecsBuilder_ == null) {
                  ensureInputFeatureColumnSpecsIsMutable();
                  inputFeatureColumnSpecs_.add(m);
                } else {
                  inputFeatureColumnSpecsBuilder_.addMessage(m);
                }
                break;
              } // case 26
            case 34:
              {
                optimizationObjective_ = input.readStringRequireUtf8();
                bitField0_ |= 0x00000010;
                break;
              } // case 34
            case 42:
              {
                com.google.cloud.automl.v1beta1.TablesModelColumnInfo m =
                    input.readMessage(
                        com.google.cloud.automl.v1beta1.TablesModelColumnInfo.parser(),
                        extensionRegistry);
                if (tablesModelColumnInfoBuilder_ == null) {
                  ensureTablesModelColumnInfoIsMutable();
                  tablesModelColumnInfo_.add(m);
                } else {
                  tablesModelColumnInfoBuilder_.addMessage(m);
                }
                break;
              } // case 42
            case 48:
              {
                trainBudgetMilliNodeHours_ = input.readInt64();
                bitField0_ |= 0x00000040;
                break;
              } // case 48
            case 56:
              {
                trainCostMilliNodeHours_ = input.readInt64();
                bitField0_ |= 0x00000080;
                break;
              } // case 56
            case 96:
              {
                disableEarlyStopping_ = input.readBool();
                bitField0_ |= 0x00000100;
                break;
              } // case 96
            case 141:
              {
                additionalOptimizationObjectiveConfig_ = input.readFloat();
                additionalOptimizationObjectiveConfigCase_ = 17;
                break;
              } // case 141
            case 149:
              {
                additionalOptimizationObjectiveConfig_ = input.readFloat();
                additionalOptimizationObjectiveConfigCase_ = 18;
                break;
              } // case 149
            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 additionalOptimizationObjectiveConfigCase_ = 0;
    private java.lang.Object additionalOptimizationObjectiveConfig_;

    public AdditionalOptimizationObjectiveConfigCase
        getAdditionalOptimizationObjectiveConfigCase() {
      return AdditionalOptimizationObjectiveConfigCase.forNumber(
          additionalOptimizationObjectiveConfigCase_);
    }

    public Builder clearAdditionalOptimizationObjectiveConfig() {
      additionalOptimizationObjectiveConfigCase_ = 0;
      additionalOptimizationObjectiveConfig_ = null;
      onChanged();
      return this;
    }

    private int bitField0_;

    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_recall_value = 17;</code>
     *
     * @return Whether the optimizationObjectiveRecallValue field is set.
     */
    public boolean hasOptimizationObjectiveRecallValue() {
      return additionalOptimizationObjectiveConfigCase_ == 17;
    }
    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_recall_value = 17;</code>
     *
     * @return The optimizationObjectiveRecallValue.
     */
    public float getOptimizationObjectiveRecallValue() {
      if (additionalOptimizationObjectiveConfigCase_ == 17) {
        return (java.lang.Float) additionalOptimizationObjectiveConfig_;
      }
      return 0F;
    }
    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_recall_value = 17;</code>
     *
     * @param value The optimizationObjectiveRecallValue to set.
     * @return This builder for chaining.
     */
    public Builder setOptimizationObjectiveRecallValue(float value) {

      additionalOptimizationObjectiveConfigCase_ = 17;
      additionalOptimizationObjectiveConfig_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_PRECISION_AT_RECALL".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_recall_value = 17;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearOptimizationObjectiveRecallValue() {
      if (additionalOptimizationObjectiveConfigCase_ == 17) {
        additionalOptimizationObjectiveConfigCase_ = 0;
        additionalOptimizationObjectiveConfig_ = null;
        onChanged();
      }
      return this;
    }

    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_precision_value = 18;</code>
     *
     * @return Whether the optimizationObjectivePrecisionValue field is set.
     */
    public boolean hasOptimizationObjectivePrecisionValue() {
      return additionalOptimizationObjectiveConfigCase_ == 18;
    }
    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_precision_value = 18;</code>
     *
     * @return The optimizationObjectivePrecisionValue.
     */
    public float getOptimizationObjectivePrecisionValue() {
      if (additionalOptimizationObjectiveConfigCase_ == 18) {
        return (java.lang.Float) additionalOptimizationObjectiveConfig_;
      }
      return 0F;
    }
    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_precision_value = 18;</code>
     *
     * @param value The optimizationObjectivePrecisionValue to set.
     * @return This builder for chaining.
     */
    public Builder setOptimizationObjectivePrecisionValue(float value) {

      additionalOptimizationObjectiveConfigCase_ = 18;
      additionalOptimizationObjectiveConfig_ = value;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required when optimization_objective is "MAXIMIZE_RECALL_AT_PRECISION".
     * Must be between 0 and 1, inclusive.
     * </pre>
     *
     * <code>float optimization_objective_precision_value = 18;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearOptimizationObjectivePrecisionValue() {
      if (additionalOptimizationObjectiveConfigCase_ == 18) {
        additionalOptimizationObjectiveConfigCase_ = 0;
        additionalOptimizationObjectiveConfig_ = null;
        onChanged();
      }
      return this;
    }

    private com.google.cloud.automl.v1beta1.ColumnSpec targetColumnSpec_;
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.ColumnSpec,
            com.google.cloud.automl.v1beta1.ColumnSpec.Builder,
            com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>
        targetColumnSpecBuilder_;
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     *
     * @return Whether the targetColumnSpec field is set.
     */
    public boolean hasTargetColumnSpec() {
      return ((bitField0_ & 0x00000004) != 0);
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     *
     * @return The targetColumnSpec.
     */
    public com.google.cloud.automl.v1beta1.ColumnSpec getTargetColumnSpec() {
      if (targetColumnSpecBuilder_ == null) {
        return targetColumnSpec_ == null
            ? com.google.cloud.automl.v1beta1.ColumnSpec.getDefaultInstance()
            : targetColumnSpec_;
      } else {
        return targetColumnSpecBuilder_.getMessage();
      }
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     */
    public Builder setTargetColumnSpec(com.google.cloud.automl.v1beta1.ColumnSpec value) {
      if (targetColumnSpecBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        targetColumnSpec_ = value;
      } else {
        targetColumnSpecBuilder_.setMessage(value);
      }
      bitField0_ |= 0x00000004;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     */
    public Builder setTargetColumnSpec(
        com.google.cloud.automl.v1beta1.ColumnSpec.Builder builderForValue) {
      if (targetColumnSpecBuilder_ == null) {
        targetColumnSpec_ = builderForValue.build();
      } else {
        targetColumnSpecBuilder_.setMessage(builderForValue.build());
      }
      bitField0_ |= 0x00000004;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     */
    public Builder mergeTargetColumnSpec(com.google.cloud.automl.v1beta1.ColumnSpec value) {
      if (targetColumnSpecBuilder_ == null) {
        if (((bitField0_ & 0x00000004) != 0)
            && targetColumnSpec_ != null
            && targetColumnSpec_
                != com.google.cloud.automl.v1beta1.ColumnSpec.getDefaultInstance()) {
          getTargetColumnSpecBuilder().mergeFrom(value);
        } else {
          targetColumnSpec_ = value;
        }
      } else {
        targetColumnSpecBuilder_.mergeFrom(value);
      }
      bitField0_ |= 0x00000004;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     */
    public Builder clearTargetColumnSpec() {
      bitField0_ = (bitField0_ & ~0x00000004);
      targetColumnSpec_ = null;
      if (targetColumnSpecBuilder_ != null) {
        targetColumnSpecBuilder_.dispose();
        targetColumnSpecBuilder_ = null;
      }
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     */
    public com.google.cloud.automl.v1beta1.ColumnSpec.Builder getTargetColumnSpecBuilder() {
      bitField0_ |= 0x00000004;
      onChanged();
      return getTargetColumnSpecFieldBuilder().getBuilder();
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     */
    public com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getTargetColumnSpecOrBuilder() {
      if (targetColumnSpecBuilder_ != null) {
        return targetColumnSpecBuilder_.getMessageOrBuilder();
      } else {
        return targetColumnSpec_ == null
            ? com.google.cloud.automl.v1beta1.ColumnSpec.getDefaultInstance()
            : targetColumnSpec_;
      }
    }
    /**
     *
     *
     * <pre>
     * Column spec of the dataset's primary table's column the model is
     * predicting. Snapshotted when model creation started.
     * Only 3 fields are used:
     * name - May be set on CreateModel, if it's not then the ColumnSpec
     *        corresponding to the current target_column_spec_id of the dataset
     *        the model is trained from is used.
     *        If neither is set, CreateModel will error.
     * display_name - Output only.
     * data_type - Output only.
     * </pre>
     *
     * <code>.google.cloud.automl.v1beta1.ColumnSpec target_column_spec = 2;</code>
     */
    private com.google.protobuf.SingleFieldBuilderV3<
            com.google.cloud.automl.v1beta1.ColumnSpec,
            com.google.cloud.automl.v1beta1.ColumnSpec.Builder,
            com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>
        getTargetColumnSpecFieldBuilder() {
      if (targetColumnSpecBuilder_ == null) {
        targetColumnSpecBuilder_ =
            new com.google.protobuf.SingleFieldBuilderV3<
                com.google.cloud.automl.v1beta1.ColumnSpec,
                com.google.cloud.automl.v1beta1.ColumnSpec.Builder,
                com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>(
                getTargetColumnSpec(), getParentForChildren(), isClean());
        targetColumnSpec_ = null;
      }
      return targetColumnSpecBuilder_;
    }

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

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

    private com.google.protobuf.RepeatedFieldBuilderV3<
            com.google.cloud.automl.v1beta1.ColumnSpec,
            com.google.cloud.automl.v1beta1.ColumnSpec.Builder,
            com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>
        inputFeatureColumnSpecsBuilder_;

    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public java.util.List<com.google.cloud.automl.v1beta1.ColumnSpec>
        getInputFeatureColumnSpecsList() {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        return java.util.Collections.unmodifiableList(inputFeatureColumnSpecs_);
      } else {
        return inputFeatureColumnSpecsBuilder_.getMessageList();
      }
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public int getInputFeatureColumnSpecsCount() {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        return inputFeatureColumnSpecs_.size();
      } else {
        return inputFeatureColumnSpecsBuilder_.getCount();
      }
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public com.google.cloud.automl.v1beta1.ColumnSpec getInputFeatureColumnSpecs(int index) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        return inputFeatureColumnSpecs_.get(index);
      } else {
        return inputFeatureColumnSpecsBuilder_.getMessage(index);
      }
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder setInputFeatureColumnSpecs(
        int index, com.google.cloud.automl.v1beta1.ColumnSpec value) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureInputFeatureColumnSpecsIsMutable();
        inputFeatureColumnSpecs_.set(index, value);
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.setMessage(index, value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder setInputFeatureColumnSpecs(
        int index, com.google.cloud.automl.v1beta1.ColumnSpec.Builder builderForValue) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        ensureInputFeatureColumnSpecsIsMutable();
        inputFeatureColumnSpecs_.set(index, builderForValue.build());
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.setMessage(index, builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder addInputFeatureColumnSpecs(com.google.cloud.automl.v1beta1.ColumnSpec value) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureInputFeatureColumnSpecsIsMutable();
        inputFeatureColumnSpecs_.add(value);
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.addMessage(value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder addInputFeatureColumnSpecs(
        int index, com.google.cloud.automl.v1beta1.ColumnSpec value) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        if (value == null) {
          throw new NullPointerException();
        }
        ensureInputFeatureColumnSpecsIsMutable();
        inputFeatureColumnSpecs_.add(index, value);
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.addMessage(index, value);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder addInputFeatureColumnSpecs(
        com.google.cloud.automl.v1beta1.ColumnSpec.Builder builderForValue) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        ensureInputFeatureColumnSpecsIsMutable();
        inputFeatureColumnSpecs_.add(builderForValue.build());
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.addMessage(builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder addInputFeatureColumnSpecs(
        int index, com.google.cloud.automl.v1beta1.ColumnSpec.Builder builderForValue) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        ensureInputFeatureColumnSpecsIsMutable();
        inputFeatureColumnSpecs_.add(index, builderForValue.build());
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.addMessage(index, builderForValue.build());
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder addAllInputFeatureColumnSpecs(
        java.lang.Iterable<? extends com.google.cloud.automl.v1beta1.ColumnSpec> values) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        ensureInputFeatureColumnSpecsIsMutable();
        com.google.protobuf.AbstractMessageLite.Builder.addAll(values, inputFeatureColumnSpecs_);
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.addAllMessages(values);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder clearInputFeatureColumnSpecs() {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        inputFeatureColumnSpecs_ = java.util.Collections.emptyList();
        bitField0_ = (bitField0_ & ~0x00000008);
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public Builder removeInputFeatureColumnSpecs(int index) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        ensureInputFeatureColumnSpecsIsMutable();
        inputFeatureColumnSpecs_.remove(index);
        onChanged();
      } else {
        inputFeatureColumnSpecsBuilder_.remove(index);
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public com.google.cloud.automl.v1beta1.ColumnSpec.Builder getInputFeatureColumnSpecsBuilder(
        int index) {
      return getInputFeatureColumnSpecsFieldBuilder().getBuilder(index);
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder getInputFeatureColumnSpecsOrBuilder(
        int index) {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        return inputFeatureColumnSpecs_.get(index);
      } else {
        return inputFeatureColumnSpecsBuilder_.getMessageOrBuilder(index);
      }
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public java.util.List<? extends com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>
        getInputFeatureColumnSpecsOrBuilderList() {
      if (inputFeatureColumnSpecsBuilder_ != null) {
        return inputFeatureColumnSpecsBuilder_.getMessageOrBuilderList();
      } else {
        return java.util.Collections.unmodifiableList(inputFeatureColumnSpecs_);
      }
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public com.google.cloud.automl.v1beta1.ColumnSpec.Builder addInputFeatureColumnSpecsBuilder() {
      return getInputFeatureColumnSpecsFieldBuilder()
          .addBuilder(com.google.cloud.automl.v1beta1.ColumnSpec.getDefaultInstance());
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public com.google.cloud.automl.v1beta1.ColumnSpec.Builder addInputFeatureColumnSpecsBuilder(
        int index) {
      return getInputFeatureColumnSpecsFieldBuilder()
          .addBuilder(index, com.google.cloud.automl.v1beta1.ColumnSpec.getDefaultInstance());
    }
    /**
     *
     *
     * <pre>
     * Column specs of the dataset's primary table's columns, on which
     * the model is trained and which are used as the input for predictions.
     * The
     * [target_column][google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec]
     * as well as, according to dataset's state upon model creation,
     * [weight_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id],
     * and
     * [ml_use_column][google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id]
     * must never be included here.
     * Only 3 fields are used:
     * * name - May be set on CreateModel, if set only the columns specified are
     *   used, otherwise all primary table's columns (except the ones listed
     *   above) are used for the training and prediction input.
     * * display_name - Output only.
     * * data_type - Output only.
     * </pre>
     *
     * <code>repeated .google.cloud.automl.v1beta1.ColumnSpec input_feature_column_specs = 3;</code>
     */
    public java.util.List<com.google.cloud.automl.v1beta1.ColumnSpec.Builder>
        getInputFeatureColumnSpecsBuilderList() {
      return getInputFeatureColumnSpecsFieldBuilder().getBuilderList();
    }

    private com.google.protobuf.RepeatedFieldBuilderV3<
            com.google.cloud.automl.v1beta1.ColumnSpec,
            com.google.cloud.automl.v1beta1.ColumnSpec.Builder,
            com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>
        getInputFeatureColumnSpecsFieldBuilder() {
      if (inputFeatureColumnSpecsBuilder_ == null) {
        inputFeatureColumnSpecsBuilder_ =
            new com.google.protobuf.RepeatedFieldBuilderV3<
                com.google.cloud.automl.v1beta1.ColumnSpec,
                com.google.cloud.automl.v1beta1.ColumnSpec.Builder,
                com.google.cloud.automl.v1beta1.ColumnSpecOrBuilder>(
                inputFeatureColumnSpecs_,
                ((bitField0_ & 0x00000008) != 0),
                getParentForChildren(),
                isClean());
        inputFeatureColumnSpecs_ = null;
      }
      return inputFeatureColumnSpecsBuilder_;
    }

    private java.lang.Object optimizationObjective_ = "";
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that maximizes/minimizes the value of the objective
     * function over the validation set.
     * The supported optimization objectives depend on the prediction type.
     * If the field is not set, a default objective function is used.
     * CLASSIFICATION_BINARY:
     *   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
     *                                 operating characteristic (ROC) curve.
     *   "MINIMIZE_LOG_LOSS" - Minimize log loss.
     *   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
     *   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
     *                                   recall value.
     *   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
     *                                    precision value.
     * CLASSIFICATION_MULTI_CLASS :
     *   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
     * REGRESSION:
     *   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
     *   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
     *   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
     * </pre>
     *
     * <code>string optimization_objective = 4;</code>
     *
     * @return The optimizationObjective.
     */
    public java.lang.String getOptimizationObjective() {
      java.lang.Object ref = optimizationObjective_;
      if (!(ref instanceof java.lang.String)) {
        com.google.protobuf.ByteString bs = (com.google.protobuf.ByteString) ref;
        java.lang.String s = bs.toStringUtf8();
        optimizationObjective_ = s;
        return s;
      } else {
        return (java.lang.String) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that maximizes/minimizes the value of the objective
     * function over the validation set.
     * The supported optimization objectives depend on the prediction type.
     * If the field is not set, a default objective function is used.
     * CLASSIFICATION_BINARY:
     *   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
     *                                 operating characteristic (ROC) curve.
     *   "MINIMIZE_LOG_LOSS" - Minimize log loss.
     *   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
     *   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
     *                                   recall value.
     *   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
     *                                    precision value.
     * CLASSIFICATION_MULTI_CLASS :
     *   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
     * REGRESSION:
     *   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
     *   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
     *   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
     * </pre>
     *
     * <code>string optimization_objective = 4;</code>
     *
     * @return The bytes for optimizationObjective.
     */
    public com.google.protobuf.ByteString getOptimizationObjectiveBytes() {
      java.lang.Object ref = optimizationObjective_;
      if (ref instanceof String) {
        com.google.protobuf.ByteString b =
            com.google.protobuf.ByteString.copyFromUtf8((java.lang.String) ref);
        optimizationObjective_ = b;
        return b;
      } else {
        return (com.google.protobuf.ByteString) ref;
      }
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that maximizes/minimizes the value of the objective
     * function over the validation set.
     * The supported optimization objectives depend on the prediction type.
     * If the field is not set, a default objective function is used.
     * CLASSIFICATION_BINARY:
     *   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
     *                                 operating characteristic (ROC) curve.
     *   "MINIMIZE_LOG_LOSS" - Minimize log loss.
     *   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
     *   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
     *                                   recall value.
     *   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
     *                                    precision value.
     * CLASSIFICATION_MULTI_CLASS :
     *   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
     * REGRESSION:
     *   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
     *   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
     *   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
     * </pre>
     *
     * <code>string optimization_objective = 4;</code>
     *
     * @param value The optimizationObjective to set.
     * @return This builder for chaining.
     */
    public Builder setOptimizationObjective(java.lang.String value) {
      if (value == null) {
        throw new NullPointerException();
      }
      optimizationObjective_ = value;
      bitField0_ |= 0x00000010;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that maximizes/minimizes the value of the objective
     * function over the validation set.
     * The supported optimization objectives depend on the prediction type.
     * If the field is not set, a default objective function is used.
     * CLASSIFICATION_BINARY:
     *   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
     *                                 operating characteristic (ROC) curve.
     *   "MINIMIZE_LOG_LOSS" - Minimize log loss.
     *   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
     *   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
     *                                   recall value.
     *   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
     *                                    precision value.
     * CLASSIFICATION_MULTI_CLASS :
     *   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
     * REGRESSION:
     *   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
     *   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
     *   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
     * </pre>
     *
     * <code>string optimization_objective = 4;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearOptimizationObjective() {
      optimizationObjective_ = getDefaultInstance().getOptimizationObjective();
      bitField0_ = (bitField0_ & ~0x00000010);
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Objective function the model is optimizing towards. The training process
     * creates a model that maximizes/minimizes the value of the objective
     * function over the validation set.
     * The supported optimization objectives depend on the prediction type.
     * If the field is not set, a default objective function is used.
     * CLASSIFICATION_BINARY:
     *   "MAXIMIZE_AU_ROC" (default) - Maximize the area under the receiver
     *                                 operating characteristic (ROC) curve.
     *   "MINIMIZE_LOG_LOSS" - Minimize log loss.
     *   "MAXIMIZE_AU_PRC" - Maximize the area under the precision-recall curve.
     *   "MAXIMIZE_PRECISION_AT_RECALL" - Maximize precision for a specified
     *                                   recall value.
     *   "MAXIMIZE_RECALL_AT_PRECISION" - Maximize recall for a specified
     *                                    precision value.
     * CLASSIFICATION_MULTI_CLASS :
     *   "MINIMIZE_LOG_LOSS" (default) - Minimize log loss.
     * REGRESSION:
     *   "MINIMIZE_RMSE" (default) - Minimize root-mean-squared error (RMSE).
     *   "MINIMIZE_MAE" - Minimize mean-absolute error (MAE).
     *   "MINIMIZE_RMSLE" - Minimize root-mean-squared log error (RMSLE).
     * </pre>
     *
     * <code>string optimization_objective = 4;</code>
     *
     * @param value The bytes for optimizationObjective to set.
     * @return This builder for chaining.
     */
    public Builder setOptimizationObjectiveBytes(com.google.protobuf.ByteString value) {
      if (value == null) {
        throw new NullPointerException();
      }
      checkByteStringIsUtf8(value);
      optimizationObjective_ = value;
      bitField0_ |= 0x00000010;
      onChanged();
      return this;
    }

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

    private void ensureTablesModelColumnInfoIsMutable() {
      if (!((bitField0_ & 0x00000020) != 0)) {
        tablesModelColumnInfo_ =
            new java.util.ArrayList<com.google.cloud.automl.v1beta1.TablesModelColumnInfo>(
                tablesModelColumnInfo_);
        bitField0_ |= 0x00000020;
      }
    }

    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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </code>
     */
    public int getTablesModelColumnInfoCount() {
      if (tablesModelColumnInfoBuilder_ == null) {
        return tablesModelColumnInfo_.size();
      } else {
        return tablesModelColumnInfoBuilder_.getCount();
      }
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </code>
     */
    public Builder clearTablesModelColumnInfo() {
      if (tablesModelColumnInfoBuilder_ == null) {
        tablesModelColumnInfo_ = java.util.Collections.emptyList();
        bitField0_ = (bitField0_ & ~0x00000020);
        onChanged();
      } else {
        tablesModelColumnInfoBuilder_.clear();
      }
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. Auxiliary information for each of the
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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
     * input_feature_column_specs with respect to this particular model.
     * </pre>
     *
     * <code>
     * repeated .google.cloud.automl.v1beta1.TablesModelColumnInfo tables_model_column_info = 5;
     * </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_ & 0x00000020) != 0),
                getParentForChildren(),
                isClean());
        tablesModelColumnInfo_ = null;
      }
      return tablesModelColumnInfoBuilder_;
    }

    private long trainBudgetMilliNodeHours_;
    /**
     *
     *
     * <pre>
     * Required. The train budget of creating this model, expressed in milli node
     * hours i.e. 1,000 value in this field means 1 node hour.
     * The training cost of the model will not exceed this budget. The final cost
     * will be attempted to be close to the budget, though may end up being (even)
     * noticeably smaller - at the backend's discretion. This especially may
     * happen when further model training ceases to provide any improvements.
     * If the budget is set to a value known to be insufficient to train a
     * model for the given dataset, the training won't be attempted and
     * will error.
     * The train budget must be between 1,000 and 72,000 milli node hours,
     * inclusive.
     * </pre>
     *
     * <code>int64 train_budget_milli_node_hours = 6;</code>
     *
     * @return The trainBudgetMilliNodeHours.
     */
    @java.lang.Override
    public long getTrainBudgetMilliNodeHours() {
      return trainBudgetMilliNodeHours_;
    }
    /**
     *
     *
     * <pre>
     * Required. The train budget of creating this model, expressed in milli node
     * hours i.e. 1,000 value in this field means 1 node hour.
     * The training cost of the model will not exceed this budget. The final cost
     * will be attempted to be close to the budget, though may end up being (even)
     * noticeably smaller - at the backend's discretion. This especially may
     * happen when further model training ceases to provide any improvements.
     * If the budget is set to a value known to be insufficient to train a
     * model for the given dataset, the training won't be attempted and
     * will error.
     * The train budget must be between 1,000 and 72,000 milli node hours,
     * inclusive.
     * </pre>
     *
     * <code>int64 train_budget_milli_node_hours = 6;</code>
     *
     * @param value The trainBudgetMilliNodeHours to set.
     * @return This builder for chaining.
     */
    public Builder setTrainBudgetMilliNodeHours(long value) {

      trainBudgetMilliNodeHours_ = value;
      bitField0_ |= 0x00000040;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Required. The train budget of creating this model, expressed in milli node
     * hours i.e. 1,000 value in this field means 1 node hour.
     * The training cost of the model will not exceed this budget. The final cost
     * will be attempted to be close to the budget, though may end up being (even)
     * noticeably smaller - at the backend's discretion. This especially may
     * happen when further model training ceases to provide any improvements.
     * If the budget is set to a value known to be insufficient to train a
     * model for the given dataset, the training won't be attempted and
     * will error.
     * The train budget must be between 1,000 and 72,000 milli node hours,
     * inclusive.
     * </pre>
     *
     * <code>int64 train_budget_milli_node_hours = 6;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearTrainBudgetMilliNodeHours() {
      bitField0_ = (bitField0_ & ~0x00000040);
      trainBudgetMilliNodeHours_ = 0L;
      onChanged();
      return this;
    }

    private long trainCostMilliNodeHours_;
    /**
     *
     *
     * <pre>
     * Output only. The actual training cost of the model, expressed in milli
     * node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
     * to not exceed the train budget.
     * </pre>
     *
     * <code>int64 train_cost_milli_node_hours = 7;</code>
     *
     * @return The trainCostMilliNodeHours.
     */
    @java.lang.Override
    public long getTrainCostMilliNodeHours() {
      return trainCostMilliNodeHours_;
    }
    /**
     *
     *
     * <pre>
     * Output only. The actual training cost of the model, expressed in milli
     * node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
     * to not exceed the train budget.
     * </pre>
     *
     * <code>int64 train_cost_milli_node_hours = 7;</code>
     *
     * @param value The trainCostMilliNodeHours to set.
     * @return This builder for chaining.
     */
    public Builder setTrainCostMilliNodeHours(long value) {

      trainCostMilliNodeHours_ = value;
      bitField0_ |= 0x00000080;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Output only. The actual training cost of the model, expressed in milli
     * node hours, i.e. 1,000 value in this field means 1 node hour. Guaranteed
     * to not exceed the train budget.
     * </pre>
     *
     * <code>int64 train_cost_milli_node_hours = 7;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearTrainCostMilliNodeHours() {
      bitField0_ = (bitField0_ & ~0x00000080);
      trainCostMilliNodeHours_ = 0L;
      onChanged();
      return this;
    }

    private boolean disableEarlyStopping_;
    /**
     *
     *
     * <pre>
     * Use the entire training budget. This disables the early stopping feature.
     * By default, the early stopping feature is enabled, which means that AutoML
     * Tables might stop training before the entire training budget has been used.
     * </pre>
     *
     * <code>bool disable_early_stopping = 12;</code>
     *
     * @return The disableEarlyStopping.
     */
    @java.lang.Override
    public boolean getDisableEarlyStopping() {
      return disableEarlyStopping_;
    }
    /**
     *
     *
     * <pre>
     * Use the entire training budget. This disables the early stopping feature.
     * By default, the early stopping feature is enabled, which means that AutoML
     * Tables might stop training before the entire training budget has been used.
     * </pre>
     *
     * <code>bool disable_early_stopping = 12;</code>
     *
     * @param value The disableEarlyStopping to set.
     * @return This builder for chaining.
     */
    public Builder setDisableEarlyStopping(boolean value) {

      disableEarlyStopping_ = value;
      bitField0_ |= 0x00000100;
      onChanged();
      return this;
    }
    /**
     *
     *
     * <pre>
     * Use the entire training budget. This disables the early stopping feature.
     * By default, the early stopping feature is enabled, which means that AutoML
     * Tables might stop training before the entire training budget has been used.
     * </pre>
     *
     * <code>bool disable_early_stopping = 12;</code>
     *
     * @return This builder for chaining.
     */
    public Builder clearDisableEarlyStopping() {
      bitField0_ = (bitField0_ & ~0x00000100);
      disableEarlyStopping_ = false;
      onChanged();
      return this;
    }

    @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.TablesModelMetadata)
  }

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

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

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

  private static final com.google.protobuf.Parser<TablesModelMetadata> PARSER =
      new com.google.protobuf.AbstractParser<TablesModelMetadata>() {
        @java.lang.Override
        public TablesModelMetadata 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<TablesModelMetadata> parser() {
    return PARSER;
  }

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

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