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DAG-oriented machine learning framework for bug-resistant, readable, efficient, maintainable and trivially deployable models in Java and other JVM languages

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// AUTOGENERATED CODE.  DO NOT MODIFY DIRECTLY!  Instead, please modify the preparer/TrivialPreparerX.ftl file.
// See the README in the module's src/template directory for details.
package com.linkedin.dagli.preparer;

import com.linkedin.dagli.transformer.PreparedTransformer8;


/**
 * Trivial "preparer" that simply returns a provided, prepared transformer of arity 8.
 * @param  the type of result produced by the prepared transformer.
 * @param  the type of prepared transformer that will be returned
 */
public class TrivialPreparer8>
    extends AbstractStreamPreparer8 {
  private final N _preparedForNewData;
  private final PreparedTransformer8 _preparedForPreparationData;

  /**
   * Creates a new instance that will "prepare" the provided transformer.
   *
   * @param prepared the transformer which will result from this preparer.
   */
  public TrivialPreparer8(N prepared) {
    this(prepared, prepared);
  }

  /**
     * Creates a new instance that will "prepare" the provided transformers.
     *
     * @param preparedForPreparationData the transformer to be used for preparation data
     * @param preparedForNewData the transformer to be used for new data
     */
  public TrivialPreparer8(
      PreparedTransformer8 preparedForPreparationData,
      N preparedForNewData) {
    _preparedForNewData = preparedForNewData;
    _preparedForPreparationData = preparedForPreparationData;
  }

  @Override
  public PreparerResultMixed, N> finish() {
    return new PreparerResultMixed<>(_preparedForPreparationData, _preparedForNewData);
  }

  @Override
  public void process(A value1, B value2, C value3, D value4, E value5, F value6, G value7, H value8) {
  }
}