* 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

And if you update that local FastAPI source code when you run that Python file again, it will use the fresh version of FastAPI you just edited.

That way, you don't have to "install" your local version to be able to test every change.

/// note | Technical Details

import org.codelibs.fess.crawler.entity.RequestData;
import org.codelibs.fess.crawler.entity.RequestData.Method;

/**
 * Builder class for creating RequestData objects using a fluent interface.
 * This class provides a simple way to construct RequestData objects with method chaining.
 *
 * <p>Usage example:</p>
 * <pre>
 * RequestData request = RequestDataBuilder.newRequestData()
 *     .method("GET")
 *     .url("https://example.com")

 * 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);

   | 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,

   
   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.

      }
    }
  }

  /**
   * 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")