public ResourceConfig getResourceConfig() {
        return this.resourceConfig;
    }
<p>
The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning
job launches.
</p>
<p>
Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes
for scratch space. If you want SageMaker to use the storage volume to store the training data, choose
<code>File</code> as the <code>TrainingInputMode</code> in the algorithm specification. For distributed training
algorithms, specify an instance count greater than 1.
</p>
<note>
<p>
If you want to use hyperparameter optimization with instance type flexibility, use
<code>HyperParameterTuningResourceConfig</code> instead.
</p>
</note>

@return The resources, including the compute instances and storage volumes, to use for the training jobs that the
        tuning job launches.</p>
        <p>
        Storage volumes store model artifacts and incremental states. Training algorithms might also use storage
        volumes for scratch space. If you want SageMaker to use the storage volume to store the training data,
        choose <code>File</code> as the <code>TrainingInputMode</code> in the algorithm specification. For
        distributed training algorithms, specify an instance count greater than 1.
        </p>
        <note>
        <p>
        If you want to use hyperparameter optimization with instance type flexibility, use
        <code>HyperParameterTuningResourceConfig</code> instead.
        </p>
aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/model/HyperParameterTrainingJobDefinition.java