TFL_AF_None),
    $x,
    TFL_AF_None),
  (TFL_HardSwishOp $x)>;

// Matching HardSwish with extra FakeQuant. These FakeQuant ops were due to
// incorrect placement in the quantization aware training.
def MatchHardSwishQuantized : Pat<
  (TFL_MulOp (TFL_DequantizeOp (TFL_QuantizeOp
    (TFL_MulOp
     $x, (TFL_DequantizeOp (TFL_QuantizeOp (TFL_AddOp
          $x,

    }

    @Test
    void testMailingListsAreInheritedButNotAggregated() throws Exception {
        PomTestWrapper pom = buildPom("mailing-lists-inheritance/child-2");
        assertEquals(1, ((List<?>) pom.getValue("mailingLists")).size());
        assertEquals("child-mailing-list", pom.getValue("mailingLists[1]/name"));
    }

    @Test
    void testPluginInheritanceOrder() throws Exception {

  - [Changes by Kind](#changes-by-kind)
    - [Deprecation](#deprecation)
    - [API Change](#api-change)
    - [Feature](#feature)
    - [Failing Test](#failing-test)
    - [Bug or Regression](#bug-or-regression)
    - [Other (Cleanup or Flake)](#other-cleanup-or-flake)
  - [Dependencies](#dependencies)
    - [Added](#added)
    - [Changed](#changed)

A discrepancy has been reported in their behavior: if the same failing test is executed, `BuildLauncher` will report a build failure but `TestLauncher` won't.
Originally, this was a design decision in order to continue the execution and run the tests in all test tasks and not stop at the first failure.
At the same time, this behavior can be confusing for users as they can experience a failing test in a successful build.

    @Unroll("Selects variant #expectedVariant using typed attribute value #attributeValue")
    @Issue("gradle/gradle#5232")
    def "can declare typed attributes without failing serialization"() {
        given:
        repository {
            'org:test:1.0' {
                variant('api') {
                    attribute('lifecycle', 'c1')
                }

### `-tf-tpu-update-embedding-enqueue-op-inputs`

_Updates inputs to TPU embedding enqueue ops depending on whether graph is in training mode or in evaluation mode._

Updates inputs to TPU embedding enqueue ops depending on whether graph
is in training mode or in evaluation mode.
### `-tf-tpu-validate-inputs`

_Validates inputs to the TPU TF/XLA bridge_

// When there is a problem, run uses t.Fatal to signify that it's an unskippable
// infrastructure error (such as failing to read an input file or the test recipe
// being malformed), or it returns a non-nil error to signify a test case error.
//
// t.Error isn't used here to give the caller the opportunity to decide whether
// the test case failing is expected before promoting it to a real test failure.
// See expectFail and -f flag.

        // Order of scheduling/execution is not guaranteed between the consumer projects
        checkExecuteTransformWorkOperations(executeWorkOps1 + executeWorkOps2, 1)
    }

    def "failing transform"() {
        createDirs("producer", "consumer")
        settingsFile << """
            include 'producer', 'consumer'
        """

        setupBuildWithColorAttributes()

// --------------------------------------------------------------------------
// In-process TensorFlow server functionality, for use in distributed training.
// A Server instance encapsulates a set of devices and a Session target that
// can participate in distributed training. A server belongs to a cluster
// (specified by a ClusterSpec), and corresponds to a particular task in a

    }

    def "gives details about failing matches when it cannot select default configuration when no match is found and default configuration is not consumable"() {
        given:
        createDirs("a", "b")
        file('settings.gradle') << "include 'a', 'b'"