task.setTitle("Gradle API " + version);

            Javadocs javadocs = extension.getJavadocs();

            // TODO: This should be part of Javadoc task
            task.getInputs().file(javadocs.getJavadocCss())
                .withPropertyName("stylesheetFile")
                .withPathSensitivity(PathSensitivity.NAME_ONLY);

        TEST_TASK_NAME ==  getTask().getName()
        getTask().getDescription() == null
        project.is( getTask().getProject())
        getTask().getStandardOutputCapture() != null
        getTask().getInputs() != null
        getTask().getOutputs() != null
        getTask().getOnlyIf() != null
        getTask().getOnlyIf().isSatisfiedBy(getTask())
    }

    def testPath() {

  LogicalResult matchAndRewrite(stablehlo::ReduceWindowOp reduce_op,
                                PatternRewriter& rewriter) const override {
    MLIRContext* ctx = reduce_op.getContext();
    ValueRange inputs = reduce_op.getInputs();
    // Only handle binary reduce ops for now
    if (inputs.size() != 1) return failure();
    Value reduce_input = inputs[0];

    RankedTensorType reduce_type =

            public TypeRegistrationAction() {
                super(registryRef, getRuleDefinition().getDescriptor());
            }

            @Override
            public List<? extends ModelReference<?>> getInputs() {
                return emptyList();
            }

            @Override
            protected void execute(ModelRuleInvoker<?> invoker, ComponentSpecFactory registry, List<ModelView<?>> inputs) {

        }
      } else {
        assert(input_sharding_type == xla::OpSharding::OTHER);
        if (partitioned_input.getInputs().size() != num_cores_per_replica)
          return tiled_sharding_mismatched(
              partitioned_input.getInputs().size());

        for (int i = 0; i < sharding.tile_assignment_devices_size(); ++i) {
          const int assigned_logical_device =

  FunctionType func_type = func.getFunctionType();
  if (llvm::ArrayRef(return_types) == func_type.getResults()) return;

  auto updated_type =
      FunctionType::get(func.getContext(), func_type.getInputs(), return_types);
  func.setType(updated_type);
}

// TODO(jpienaar): Remove when recursive side-effect modeling is added.
bool IsSideEffectFree(func::FuncOp func) {
  return !func.getBody()

        // A further work around for missing ability to skip task when input file is missing (see #getInputFileIfExists below)
        getInputs().file(executable);
        this.targetPlatform = objectFactory.property(NativePlatform.class);
        this.toolChain = objectFactory.property(NativeToolChain.class);
    }

    /**

        this.toolChain = objectFactory.property(NativeToolChain.class);
        incrementalCompiler = getIncrementalCompilerBuilder().newCompiler(this, source, includes, macros, Providers.FALSE);
        getInputs().property("outputType", new Callable<String>() {
            @Override
            public String call() {
                NativeToolChainInternal nativeToolChain = (NativeToolChainInternal) toolChain.get();

        TEST_TASK_NAME.equals(getTask().getName())
        getTask().getDescription() == null
        project.is(getTask().getProject())
        getTask().getStandardOutputCapture() != null
        getTask().getInputs() != null
        getTask().getOutputs() != null
        getTask().getOnlyIf() != null
        getTask().getOnlyIf().isSatisfiedBy(getTask())
    }

    def "test Path"() {
        given:

    SmallVectorImpl<Value>* inputs, NamedAttrList* arg_attrs,
    llvm::StringMap<Attribute>* derived_attrs) const {
  for (const auto& operand :
       llvm::enumerate(signature.getFunctionType().getInputs())) {
    // If the index is larger than the operand number of the call_op, the
    // default value of the operand needs to be used.
    if (operand.index() >= call_op.getNumOperands()) {