assertProblemFree(collector);

        List<Dependency> rDeps = result.getDependencies();
        assertNotNull(rDeps);
        assertEquals(1, rDeps.size());
        assertEquals(
                new File(basedir, "artifact.jar").getAbsolutePath(),
                new File(rDeps.get(0).getSystemPath()).getAbsolutePath());
    }

    @Test

  # Find all one-step dependencies of those tests which are from //tensorflow
  # (since external deps will come from Python-level pip dependencies),
  # excluding dependencies and files that are known to be unneccessary.
  # This creates a list of targets under //tensorflow that are required for
  # TensorFlow python tests.

<?xml version="1.0" encoding="UTF-8"?>
<project name="deps" basedir=".">
	<property name="target.dir" value="${basedir}/target/deps" />
	<property name="webinf.dir" value="${basedir}/src/main/webapp/WEB-INF" />
	<property name="crawler.dir" value="${basedir}/src/main/webapp/WEB-INF/env/crawler" />
	<property name="suggest.dir" value="${basedir}/src/main/webapp/WEB-INF/env/suggest" />
	<property name="thumbnail.dir" value="${basedir}/src/main/webapp/WEB-INF/env/thumbnail" />

  @Benchmark
  int branchFreeLtIntInlined(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & SAMPLE_MASK;
      int x = xInts[j];
      int y = yInts[j];
      int z = constant[j];
      tmp += z + ((x - y) >>> (Integer.SIZE - 1));
    }
    return tmp;
  }

  @Benchmark
  int branchFreeLtInt(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & SAMPLE_MASK;

      ints[i] = RANDOM_SOURCE.nextInt();
    }
  }

  @Benchmark
  int pow(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += IntMath.pow(positive[j], exponent[j]);
    }
    return tmp;
  }

  @Benchmark
  int mod(int reps) {
    int tmp = 0;
    for (int i = 0; i < reps; i++) {
      int j = i & ARRAY_MASK;
      tmp += IntMath.mod(ints[j], positive[j]);

  }

  @Benchmark
  int asciiStringToUpperCase(int reps) {
    String string = noWorkToDo ? Ascii.toUpperCase(testString) : testString;

    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Ascii.toUpperCase(string).length();
    }
    return dummy;
  }

  @Benchmark
  int asciiCharSequenceToUpperCase(int reps) {

  int hashString_4(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Objects.hashCode(S0, S1, S2, S3);
    }
    return dummy;
  }

  @Benchmark
  int hashString_5(int reps) {
    int dummy = 0;
    for (int i = 0; i < reps; i++) {
      dummy += Objects.hashCode(S0, S1, S2, S3, S4);
    }
    return dummy;
  }

  @Benchmark
  int hashMixed_5(int reps) {
    int dummy = 0;

      String unused = interner.intern(Double.toHexString(Math.random()));
    }
    return reps;
  }

  @CanIgnoreReturnValue
  @Benchmark
  int stringIntern(int reps) {
    for (int i = 0; i < reps; i++) {
      String unused = Double.toHexString(Math.random()).intern();
    }
    return reps;
  }

    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(1, 2, dataset(i));
    }
    return dummy;
  }

  @Benchmark
  double percentile90(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(90, 100, dataset(i));
    }

    return datasets[i & 0xFF].clone();
  }

  @Benchmark
  double median(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(1, 2, dataset(i));
    }
    return dummy;
  }

  @Benchmark
  double percentile90(int reps) {
    double dummy = 0.0;
    for (int i = 0; i < reps; i++) {
      dummy += algorithm.singleQuantile(90, 100, dataset(i));
    }