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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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package com.linkedin.dagli.view;

import com.linkedin.dagli.producer.ChildProducer;
import com.linkedin.dagli.producer.ProducerType;
import com.linkedin.dagli.transformer.PreparedTransformer;
import com.linkedin.dagli.view.internal.TransformerViewInternalAPI;


/**
 * {@link TransformerView}s can be prepared to create a result that is derived from the prepared transformer created by
 * preparing a {@link com.linkedin.dagli.transformer.PreparableTransformer}.  This result is made available in the
 * preared DAG as a {@link com.linkedin.dagli.generator.Constant}.
 *
 * A common use for {@link TransformerView}s is to "pull out" the trained parameters from a statistical model and
 * provide them as inputs to other transformers in the DAG.  For example, after training a linear model, a
 * {@link TransformerView} can extract the K highest (absolute) weight features, which can then be used for feature
 * selection for the features passed to a subsequent model.
 *
 * @param  the type of value that will be provided when this View is prepared
 * @param  the type of the prepared transformer (for new data) that will be prepared from T
 */
public interface TransformerView>
    extends ChildProducer, ProducerType> {
  @Override
  TransformerViewInternalAPI> internalAPI();

  /**
   * Casts a producer to an effective "supertype" interface.  The semantics of the producer guarantee that the returned
   * type is valid for the instance.
   *
   * Note that although this and other {@code cast(...)} methods are safe, this safety extends only to the
   * interfaces for which they are implemented.  The covariance and contravariance relationships existing for these interfaces
   * do not necessarily hold for their derived classes.  For example, a {@code PreparedTransformer} is also a
   * {@code PreparedTransformer