/*
 * Copyright 2018-2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
 * 
 * Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
 * the License. A copy of the License is located at
 * 
 * http://aws.amazon.com/apache2.0
 * 
 * or in the "license" file accompanying this file. This file 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.
 */
package com.amazonaws.services.sagemaker.model;

import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;

/**
 * <p>
 * Specifies the configuration for a hyperparameter tuning job that uses one or more previous hyperparameter tuning jobs
 * as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to
 * search over in the new tuning job.
 * </p>
 * <p>
 * All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric, and the
 * training job that performs the best is compared to the best training jobs from the parent tuning jobs. From these,
 * the training job that performs the best as measured by the objective metric is returned as the overall best training
 * job.
 * </p>
 * <note>
 * <p>
 * All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against
 * the limit of training jobs for the tuning job.
 * </p>
 * </note>
 * 
 * @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/HyperParameterTuningJobWarmStartConfig"
 *      target="_top">AWS API Documentation</a>
 */
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class HyperParameterTuningJobWarmStartConfig implements Serializable, Cloneable, StructuredPojo {

    /**
     * <p>
     * An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job.
     * For more information about warm starting a hyperparameter tuning job, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a Previous
     * Hyperparameter Tuning Job as a Starting Point</a>.
     * </p>
     * <p>
     * Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning
     * jobs.
     * </p>
     */
    private java.util.List<ParentHyperParameterTuningJob> parentHyperParameterTuningJobs;
    /**
     * <p>
     * Specifies one of the following:
     * </p>
     * <dl>
     * <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can
     * change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning
     * job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do
     * not affect the algorithm itself. For example, changes that improve logging or adding support for a different data
     * format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but
     * the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The
     * objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * <dt>TRANSFER_LEARNING</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent
     * training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter
     * tuning jobs. The training image can also be a different version from the version used in the parent
     * hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to
     * tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent
     * jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * </dl>
     */
    private String warmStartType;

    /**
     * <p>
     * An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job.
     * For more information about warm starting a hyperparameter tuning job, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a Previous
     * Hyperparameter Tuning Job as a Starting Point</a>.
     * </p>
     * <p>
     * Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning
     * jobs.
     * </p>
     * 
     * @return An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter
     *         tuning job. For more information about warm starting a hyperparameter tuning job, see <a
     *         href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a
     *         Previous Hyperparameter Tuning Job as a Starting Point</a>.</p>
     *         <p>
     *         Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start
     *         tuning jobs.
     */

    public java.util.List<ParentHyperParameterTuningJob> getParentHyperParameterTuningJobs() {
        return parentHyperParameterTuningJobs;
    }

    /**
     * <p>
     * An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job.
     * For more information about warm starting a hyperparameter tuning job, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a Previous
     * Hyperparameter Tuning Job as a Starting Point</a>.
     * </p>
     * <p>
     * Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning
     * jobs.
     * </p>
     * 
     * @param parentHyperParameterTuningJobs
     *        An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter
     *        tuning job. For more information about warm starting a hyperparameter tuning job, see <a
     *        href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a
     *        Previous Hyperparameter Tuning Job as a Starting Point</a>.</p>
     *        <p>
     *        Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start
     *        tuning jobs.
     */

    public void setParentHyperParameterTuningJobs(java.util.Collection<ParentHyperParameterTuningJob> parentHyperParameterTuningJobs) {
        if (parentHyperParameterTuningJobs == null) {
            this.parentHyperParameterTuningJobs = null;
            return;
        }

        this.parentHyperParameterTuningJobs = new java.util.ArrayList<ParentHyperParameterTuningJob>(parentHyperParameterTuningJobs);
    }

    /**
     * <p>
     * An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job.
     * For more information about warm starting a hyperparameter tuning job, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a Previous
     * Hyperparameter Tuning Job as a Starting Point</a>.
     * </p>
     * <p>
     * Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning
     * jobs.
     * </p>
     * <p>
     * <b>NOTE:</b> This method appends the values to the existing list (if any). Use
     * {@link #setParentHyperParameterTuningJobs(java.util.Collection)} or
     * {@link #withParentHyperParameterTuningJobs(java.util.Collection)} if you want to override the existing values.
     * </p>
     * 
     * @param parentHyperParameterTuningJobs
     *        An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter
     *        tuning job. For more information about warm starting a hyperparameter tuning job, see <a
     *        href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a
     *        Previous Hyperparameter Tuning Job as a Starting Point</a>.</p>
     *        <p>
     *        Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start
     *        tuning jobs.
     * @return Returns a reference to this object so that method calls can be chained together.
     */

    public HyperParameterTuningJobWarmStartConfig withParentHyperParameterTuningJobs(ParentHyperParameterTuningJob... parentHyperParameterTuningJobs) {
        if (this.parentHyperParameterTuningJobs == null) {
            setParentHyperParameterTuningJobs(new java.util.ArrayList<ParentHyperParameterTuningJob>(parentHyperParameterTuningJobs.length));
        }
        for (ParentHyperParameterTuningJob ele : parentHyperParameterTuningJobs) {
            this.parentHyperParameterTuningJobs.add(ele);
        }
        return this;
    }

    /**
     * <p>
     * An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job.
     * For more information about warm starting a hyperparameter tuning job, see <a
     * href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a Previous
     * Hyperparameter Tuning Job as a Starting Point</a>.
     * </p>
     * <p>
     * Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning
     * jobs.
     * </p>
     * 
     * @param parentHyperParameterTuningJobs
     *        An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter
     *        tuning job. For more information about warm starting a hyperparameter tuning job, see <a
     *        href="https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-warm-start.html">Using a
     *        Previous Hyperparameter Tuning Job as a Starting Point</a>.</p>
     *        <p>
     *        Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start
     *        tuning jobs.
     * @return Returns a reference to this object so that method calls can be chained together.
     */

    public HyperParameterTuningJobWarmStartConfig withParentHyperParameterTuningJobs(
            java.util.Collection<ParentHyperParameterTuningJob> parentHyperParameterTuningJobs) {
        setParentHyperParameterTuningJobs(parentHyperParameterTuningJobs);
        return this;
    }

    /**
     * <p>
     * Specifies one of the following:
     * </p>
     * <dl>
     * <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can
     * change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning
     * job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do
     * not affect the algorithm itself. For example, changes that improve logging or adding support for a different data
     * format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but
     * the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The
     * objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * <dt>TRANSFER_LEARNING</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent
     * training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter
     * tuning jobs. The training image can also be a different version from the version used in the parent
     * hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to
     * tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent
     * jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * </dl>
     * 
     * @param warmStartType
     *        Specifies one of the following:</p>
     *        <dl>
     *        <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     *        <dd>
     *        <p>
     *        The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs.
     *        You can change the hyperparameter ranges to search and the maximum number of training jobs that the
     *        hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the
     *        changes in the new version do not affect the algorithm itself. For example, changes that improve logging
     *        or adding support for a different data format are allowed. You can also change hyperparameters from
     *        tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters
     *        must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the
     *        same as for all parent jobs.
     *        </p>
     *        </dd>
     *        <dt>TRANSFER_LEARNING</dt>
     *        <dd>
     *        <p>
     *        The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of
     *        concurrent training jobs, and maximum number of training jobs that are different than those of its parent
     *        hyperparameter tuning jobs. The training image can also be a different version from the version used in
     *        the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from
     *        static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it
     *        is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent
     *        jobs.
     *        </p>
     *        </dd>
     * @see HyperParameterTuningJobWarmStartType
     */

    public void setWarmStartType(String warmStartType) {
        this.warmStartType = warmStartType;
    }

    /**
     * <p>
     * Specifies one of the following:
     * </p>
     * <dl>
     * <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can
     * change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning
     * job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do
     * not affect the algorithm itself. For example, changes that improve logging or adding support for a different data
     * format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but
     * the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The
     * objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * <dt>TRANSFER_LEARNING</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent
     * training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter
     * tuning jobs. The training image can also be a different version from the version used in the parent
     * hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to
     * tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent
     * jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * </dl>
     * 
     * @return Specifies one of the following:</p>
     *         <dl>
     *         <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     *         <dd>
     *         <p>
     *         The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs.
     *         You can change the hyperparameter ranges to search and the maximum number of training jobs that the
     *         hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the
     *         changes in the new version do not affect the algorithm itself. For example, changes that improve logging
     *         or adding support for a different data format are allowed. You can also change hyperparameters from
     *         tunable to static, and from static to tunable, but the total number of static plus tunable
     *         hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning
     *         job must be the same as for all parent jobs.
     *         </p>
     *         </dd>
     *         <dt>TRANSFER_LEARNING</dt>
     *         <dd>
     *         <p>
     *         The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of
     *         concurrent training jobs, and maximum number of training jobs that are different than those of its parent
     *         hyperparameter tuning jobs. The training image can also be a different version from the version used in
     *         the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and
     *         from static to tunable, but the total number of static plus tunable hyperparameters must remain the same
     *         as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all
     *         parent jobs.
     *         </p>
     *         </dd>
     * @see HyperParameterTuningJobWarmStartType
     */

    public String getWarmStartType() {
        return this.warmStartType;
    }

    /**
     * <p>
     * Specifies one of the following:
     * </p>
     * <dl>
     * <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can
     * change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning
     * job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do
     * not affect the algorithm itself. For example, changes that improve logging or adding support for a different data
     * format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but
     * the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The
     * objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * <dt>TRANSFER_LEARNING</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent
     * training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter
     * tuning jobs. The training image can also be a different version from the version used in the parent
     * hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to
     * tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent
     * jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * </dl>
     * 
     * @param warmStartType
     *        Specifies one of the following:</p>
     *        <dl>
     *        <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     *        <dd>
     *        <p>
     *        The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs.
     *        You can change the hyperparameter ranges to search and the maximum number of training jobs that the
     *        hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the
     *        changes in the new version do not affect the algorithm itself. For example, changes that improve logging
     *        or adding support for a different data format are allowed. You can also change hyperparameters from
     *        tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters
     *        must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the
     *        same as for all parent jobs.
     *        </p>
     *        </dd>
     *        <dt>TRANSFER_LEARNING</dt>
     *        <dd>
     *        <p>
     *        The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of
     *        concurrent training jobs, and maximum number of training jobs that are different than those of its parent
     *        hyperparameter tuning jobs. The training image can also be a different version from the version used in
     *        the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from
     *        static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it
     *        is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent
     *        jobs.
     *        </p>
     *        </dd>
     * @return Returns a reference to this object so that method calls can be chained together.
     * @see HyperParameterTuningJobWarmStartType
     */

    public HyperParameterTuningJobWarmStartConfig withWarmStartType(String warmStartType) {
        setWarmStartType(warmStartType);
        return this;
    }

    /**
     * <p>
     * Specifies one of the following:
     * </p>
     * <dl>
     * <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can
     * change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning
     * job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do
     * not affect the algorithm itself. For example, changes that improve logging or adding support for a different data
     * format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but
     * the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The
     * objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * <dt>TRANSFER_LEARNING</dt>
     * <dd>
     * <p>
     * The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent
     * training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter
     * tuning jobs. The training image can also be a different version from the version used in the parent
     * hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to
     * tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent
     * jobs. The objective metric for the new tuning job must be the same as for all parent jobs.
     * </p>
     * </dd>
     * </dl>
     * 
     * @param warmStartType
     *        Specifies one of the following:</p>
     *        <dl>
     *        <dt>IDENTICAL_DATA_AND_ALGORITHM</dt>
     *        <dd>
     *        <p>
     *        The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs.
     *        You can change the hyperparameter ranges to search and the maximum number of training jobs that the
     *        hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the
     *        changes in the new version do not affect the algorithm itself. For example, changes that improve logging
     *        or adding support for a different data format are allowed. You can also change hyperparameters from
     *        tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters
     *        must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the
     *        same as for all parent jobs.
     *        </p>
     *        </dd>
     *        <dt>TRANSFER_LEARNING</dt>
     *        <dd>
     *        <p>
     *        The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of
     *        concurrent training jobs, and maximum number of training jobs that are different than those of its parent
     *        hyperparameter tuning jobs. The training image can also be a different version from the version used in
     *        the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from
     *        static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it
     *        is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent
     *        jobs.
     *        </p>
     *        </dd>
     * @return Returns a reference to this object so that method calls can be chained together.
     * @see HyperParameterTuningJobWarmStartType
     */

    public HyperParameterTuningJobWarmStartConfig withWarmStartType(HyperParameterTuningJobWarmStartType warmStartType) {
        this.warmStartType = warmStartType.toString();
        return this;
    }

    /**
     * Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be
     * redacted from this string using a placeholder value.
     *
     * @return A string representation of this object.
     *
     * @see java.lang.Object#toString()
     */
    @Override
    public String toString() {
        StringBuilder sb = new StringBuilder();
        sb.append("{");
        if (getParentHyperParameterTuningJobs() != null)
            sb.append("ParentHyperParameterTuningJobs: ").append(getParentHyperParameterTuningJobs()).append(",");
        if (getWarmStartType() != null)
            sb.append("WarmStartType: ").append(getWarmStartType());
        sb.append("}");
        return sb.toString();
    }

    @Override
    public boolean equals(Object obj) {
        if (this == obj)
            return true;
        if (obj == null)
            return false;

        if (obj instanceof HyperParameterTuningJobWarmStartConfig == false)
            return false;
        HyperParameterTuningJobWarmStartConfig other = (HyperParameterTuningJobWarmStartConfig) obj;
        if (other.getParentHyperParameterTuningJobs() == null ^ this.getParentHyperParameterTuningJobs() == null)
            return false;
        if (other.getParentHyperParameterTuningJobs() != null
                && other.getParentHyperParameterTuningJobs().equals(this.getParentHyperParameterTuningJobs()) == false)
            return false;
        if (other.getWarmStartType() == null ^ this.getWarmStartType() == null)
            return false;
        if (other.getWarmStartType() != null && other.getWarmStartType().equals(this.getWarmStartType()) == false)
            return false;
        return true;
    }

    @Override
    public int hashCode() {
        final int prime = 31;
        int hashCode = 1;

        hashCode = prime * hashCode + ((getParentHyperParameterTuningJobs() == null) ? 0 : getParentHyperParameterTuningJobs().hashCode());
        hashCode = prime * hashCode + ((getWarmStartType() == null) ? 0 : getWarmStartType().hashCode());
        return hashCode;
    }

    @Override
    public HyperParameterTuningJobWarmStartConfig clone() {
        try {
            return (HyperParameterTuningJobWarmStartConfig) super.clone();
        } catch (CloneNotSupportedException e) {
            throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
        }
    }

    @com.amazonaws.annotation.SdkInternalApi
    @Override
    public void marshall(ProtocolMarshaller protocolMarshaller) {
        com.amazonaws.services.sagemaker.model.transform.HyperParameterTuningJobWarmStartConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
    }
}
