xsi:schemaLocation="http://maven.apache.org/TOOLCHAINS/1.1.0 http://maven.apache.org/xsd/toolchains-1.1.0.xsd">

  <!--
   | With toolchains you can refer to installations on your system. This
   | way you don't have to hardcode paths in your pom.xml.
   |
   | Every toolchain consist of 3 elements:
   | * type: the type of tool. An often used value is 'jdk'. Toolchains-aware

## 1. Prerequisites

* Familiarity with [Docker Compose](https://docs.docker.com/compose/overview/).

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

 * Generates tests of all methods on a {@code ByteSink} given various inputs written to it as well
 * as sub-suites for testing the {@code CharSink} view in the same way.
 *
 * @author Colin Decker
 */
@AndroidIncompatible // TODO(b/230620681): Make this available (even though we won't run it).
@NullUnmarked

 * Generates tests of all methods on a {@code ByteSink} given various inputs written to it as well
 * as sub-suites for testing the {@code CharSink} view in the same way.
 *
 * @author Colin Decker
 */
@AndroidIncompatible // TODO(b/230620681): Make this available (even though we won't run it).
@NullUnmarked

   | dereferenced during the build process to configure the cactus plugin.
   |
   | As noted above, profiles can be activated in a variety of ways. One way - the activeProfiles
   | section of this document (settings.xml) - will be discussed later. Another way essentially
   | relies on the detection of a property, either matching a particular value for the property,

 * href="https://en.wikipedia.org/wiki/Graph_(discrete_mathematics)">graph</a>-structured data.
 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * topological sort) that only need a way of accessing the predecessors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * {@snippet :

 *
 * <p>This interface is meant to be used as the type of a parameter to graph algorithms (such as
 * breadth first traversal) that only need a way of accessing the successors of a node in a graph.
 *
 * <h3>Usage</h3>
 *
 * Given an algorithm, for example:
 *
 * {@snippet :
 * public <N> someGraphAlgorithm(N startNode, SuccessorsFunction<N> successorsFunction);

      }
    }
  }

  /**
   * Implementation of the InvertedMinMaxPriorityQueue which forwards all calls to a
   * MinMaxPriorityQueue, except poll, which is forwarded to pollMax. That way we can benchmark
   * pollMax using the same code that benchmarks poll.
   */
  static final class InvertedMinMaxPriorityQueue<T> extends ForwardingQueue<T> {
    MinMaxPriorityQueue<T> mmHeap;

  }

  @CollectionSize.Require(SEVERAL)
  @MapFeature.Require(SUPPORTS_REMOVE)
  /*
   * ListMultimap.asMap essentially returns a Map<K, List<V>>; we just can't declare it that way.
   * Thus, calls like asMap().values().remove(someList) are safe because they are comparing a list
   * to a collection of other lists.
   */
  @SuppressWarnings("CollectionUndefinedEquality")