}

    static TestStartEvent createTestStartEvent() {
        new TestStartEvent(new Date().time)
    }

    static TestResult createTestResult() {
        new DefaultTestResult(TestResult.ResultType.SUCCESS, new Date().time, new Date().time, 2L, 2L, 0L, [])
    }

    static TestCompleteEvent createTestCompleteEvent() {
        new TestCompleteEvent(new Date().time)
    }

                return new TypedRecord<D, R>(
                    descriptor,
                    BuildOperationTypes.detailsType(buildOperationType).cast(descriptor.getDetails()),
                    BuildOperationTypes.resultType(buildOperationType).cast(result),
                    failure
                );
            }

        }

        public static class TypedRecord<D, R> {

)

var Analyzer = &analysis.Analyzer{
	Name:       "ctrlflow",
	Doc:        "build a control-flow graph",
	URL:        "https://pkg.go.dev/golang.org/x/tools/go/analysis/passes/ctrlflow",
	Run:        run,
	ResultType: reflect.TypeOf(new(CFGs)),
	FactTypes:  []analysis.Fact{new(noReturn)},
	Requires:   []*analysis.Analyzer{inspect.Analyzer},
}

// noReturn is a fact indicating that a function does not return.
type noReturn struct{}

  // result of 'resultType'. If a conversion can not be generated, nullptr
  // should be returned.
  Operation *materializeCallConversion(OpBuilder &builder, Value input,
                                       Type result_type,
                                       Location conversion_loc) const final {
    if (!mlir::isa<TensorType>(result_type) ||
        !mlir::isa<TensorType>(input.getType()))

                              PatternRewriter* rewriter) {
  auto shape_tensor = createI64ConstantOp(
      tensorflow::ConvertMlirShapeToTF(shape), loc, rewriter);
  Type resultType = RankedTensorType::get(shape, element_type);
  return rewriter->create<TF::ReshapeOp>(loc, resultType, /*tensor=*/value,
                                         /*shape=*/shape_tensor);
}

// Creates ReshapeOp with runtime calcuation of required shape to support

  // result of 'resultType'. If a conversion can not be generated, nullptr
  // should be returned.
  // This is just re-using the same logic as the TensorFlow dialect right now.
  Operation* materializeCallConversion(OpBuilder& builder, Value input,
                                       Type result_type,
                                       Location conversion_loc) const final {

                           llvm::function_ref<APFloat(APFloat)> calculate) {
  assert(IsF32ShapedType(result_type) || IsBF16ShapedType(result_type));
  auto result_shape_type = result_type.cast<ShapedType>();

  if (!result_shape_type.hasStaticShape()) return {};

  if (auto dense_elements = operand.dyn_cast_or_null<DenseElementsAttr>()) {
    SmallVector<APFloat, 16> new_values;

  // result of 'resultType'. If a conversion can not be generated, nullptr
  // should be returned.
  Operation *materializeCallConversion(OpBuilder &builder, Value input,
                                       Type result_type,
                                       Location conversion_loc) const final {
    if (!input.getType().isa<IntegerType>() ||
        !result_type.isa<IntegerType>()) {
      return nullptr;

def TFL_BroadcastableBinaryBuilder :
  OpBuilder<(ins "Value":$lhs, "Value":$rhs),
  [{
    auto resultType =
      OpTrait::util::getBroadcastedType(lhs.getType(), rhs.getType());
    if (!resultType)
      mlir::emitError($_state.location, "non-broadcastable operands");
    $_state.addOperands({lhs, rhs});
    $_state.types.push_back(resultType);
  }]>;

def TFL_FusedBroadcastableBinaryBuilder :

	Doc:        analysisutil.MustExtractDoc(doc, "printf"),
	URL:        "https://pkg.go.dev/golang.org/x/tools/go/analysis/passes/printf",
	Requires:   []*analysis.Analyzer{inspect.Analyzer},
	Run:        run,
	ResultType: reflect.TypeOf((*Result)(nil)),
	FactTypes:  []analysis.Fact{new(isWrapper)},
}

// Kind is a kind of fmt function behavior.
type Kind int

const (
	KindNone   Kind = iota // not a fmt wrapper function