reports.xml.outputType == Report.OutputType.FILE
            !reports.html.required.get()
            !reports.html.outputLocation.isPresent()
            reports.html.outputType == Report.OutputType.FILE
            !reports.sarif.required.get()
            !reports.sarif.outputLocation.isPresent()
            reports.sarif.outputType == Report.OutputType.FILE
            !ignoreFailures

    private static class CastingTransformer<O, I> implements InternalTransformer<O, I> {
        final Class<O> outputType;

        public CastingTransformer(Class<O> outputType) {
            this.outputType = outputType;
        }

        @Override
        public O transform(I input) {
            return Cast.cast(outputType, input);
        }
    }

    public static <T> InternalTransformer<String, T> asString() {

     *
     * @param outputType The type to cast the input to
     * @param object The object to be cast (must not be {@code null})
     * @param <O> The type to be cast to
     * @param <I> The type of the object to be vast
     * @return The input object, cast to the output type
     */
    public static <O, I> O cast(Class<O> outputType, I object) {
        try {

    }

    private static class CastingTransformer<O, I> implements Transformer<O, I> {
        final Class<O> outputType;

        public CastingTransformer(Class<O> outputType) {
            this.outputType = outputType;
        }

        @Override
        public O transform(I input) {
            return Cast.cast(outputType, input);
        }
    }

    public static <T> Transformer<String, T> asString() {

                                              RankedTensorType output_type,
                                              int64_t axis) {
  const auto outer_dims = output_type.getShape().take_front(axis);
  const int64_t outer_size = std::accumulate(
      outer_dims.begin(), outer_dims.end(), 1, std::multiplies<int64_t>());

  const auto base_inner_dims = output_type.getShape().drop_front(axis + 1);
  const int64_t base_inner_size =

	docker buildx build --provenance=false --sbom=false --pull --output=type=${OUTPUT_TYPE} --platform ${OS}/$(ARCH) \
		-t $(IMAGE):$(TAG)-${OS}-$(ARCH) --build-arg BASE=${BASE} --build-arg ARCH=$(ARCH) .
	touch $@

.container-windows-$(ARCH): $(foreach binary, ${BIN}, bin/${binary}-${OS}-${ARCH})
	docker buildx build --provenance=false --sbom=false --pull --output=type=${OUTPUT_TYPE} --platform ${OS}/$(ARCH) \

    // before broadcasting.
    if (operand_type.getRank() < output_type.getRank()) {
      input = InsertExpandDimsOp(op, rewriter, input, output_type.getRank());
    }

    SmallVector<int32_t> broadcast_shape =
        CastI64ArrayToI32(output_type.getShape()).value();
    TensorType broadcast_shape_type =
        output_type.cloneWith({output_type.getRank()}, rewriter.getI32Type());
    auto broadcast_shape_attr =

        outputs.fileProperties*.propertyName == ['$1']
        outputs.fileProperties*.propertyFiles*.files.flatten() == [file("a")]
        outputs.fileProperties*.outputFile == [file("a")]
        outputs.fileProperties*.outputType == [FILE]
    }

    def "can register output file with property name"() {
        when:
        outputs.file("a").withPropertyName("prop")
        then:
        outputs.files.files == files('a')

    }

    public TaskGeneratedReport(final String name, OutputType outputType, final Task task) {
        super(name, new Describable() {
            @Override
            public String getDisplayName() {
                return calculateDisplayName(name, task);
            }
        }, outputType);
    }

      auto *value_op = assign_variable_op.getValue().getDefiningOp();
      auto dq_op = dyn_cast_or_null<DequantizeOp>(value_op);
      if (dq_op) {
        Type output_type = dq_op.getInput().getType();
        auto qtype = quant::QuantizedType::getQuantizedElementType(output_type);
        if (qtype == quant::QuantizedType::getQuantizedElementType(ref_qtype)) {
          // Same quantization parameters, remove it.