Parameter param = params.get(i);
      Object arg = args.get(i);
      // Use new fresh value generator because 'args' were populated with new fresh generator each.
      // Two newFreshValueGenerator() instances should normally generate equal value sequence.
      Object shouldBeEqualArg = generateDummyArg(param, newFreshValueGenerator());
      if (arg != shouldBeEqualArg
          && Objects.equal(arg, shouldBeEqualArg)

        final StringBuilder buf = new StringBuilder(100);
        buf.append(methodName).append("(");
        if (methodArgs != null && methodArgs.length > 0) {
            for (final Object arg : methodArgs) {
                buf.append(arg == null ? null : arg.getClass().getName()).append(", ");
            }
            buf.setLength(buf.length() - 2);
        }
        buf.append(")");
        return buf.toString();
    }

TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_cpu"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=tensorflow/build:2.16-python${TFCI_PYTHON_VERSION}
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--build-arg PYTHON_VERSION=python$TFCI_PYTHON_VERSION --target=devel tensorflow/tools/tf_sig_build_dockerfiles"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-x86_64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1

        type_annotation = annotated_args[0]
        fastapi_annotations = [
            arg
            for arg in annotated_args[1:]
            if isinstance(arg, (FieldInfo, params.Depends))
        ]
        fastapi_specific_annotations = [
            arg
            for arg in fastapi_annotations
            if isinstance(arg, (params.Param, params.Body, params.Depends))
        ]
        if fastapi_specific_annotations:

      Parameter param = params.get(i);
      Object arg = args.get(i);
      // Use new fresh value generator because 'args' were populated with new fresh generator each.
      // Two newFreshValueGenerator() instances should normally generate equal value sequence.
      Object shouldBeEqualArg = generateDummyArg(param, newFreshValueGenerator());
      if (arg != shouldBeEqualArg
          && Objects.equal(arg, shouldBeEqualArg)

         */
        public int add2(final int arg1, final int arg2) {
            return arg1 + arg2;
        }

        /**
         * @param arg
         * @return Integer
         */
        public Integer echo(final Integer arg) {
            return arg;
        }

        /**
         *
         */
        public void throwException() {
            throw new IllegalStateException("hoge");

            argumentsToParameters.entries.firstOrNull { (arg, _) ->
                when (arg) {
                    // TODO: better to utilize. See `createArgumentMapping` in [KtFirCallResolver]
                    is FirNamedArgumentExpression, is FirSpreadArgumentExpression ->
                        arg.psi == argumentExpression.parent
                    else ->
                        arg.psi == argumentExpression
                }

                                                  TF_Status* status) {
  auto* cc_ctx = reinterpret_cast<tensorflow::OpKernelContext*>(ctx);
  const tensorflow::Tensor& arg_tensor = cc_ctx->input(index);
  absl::Status cc_status;
  if (arg_tensor.dtype() != tensorflow::DT_RESOURCE) {
    cc_status = absl::InvalidArgumentError(
        absl::StrCat("Trying to obtain resource handle from Input[", index,

 *       garbage collection, <strong>AND</strong> this stream was not constructed with {@linkplain
 *       #FileBackedOutputStream(int) the 1-arg constructor} or the {@linkplain
 *       #FileBackedOutputStream(int, boolean) 2-arg constructor} passing {@code false} in the
 *       second parameter.
 * </ul>
 *
 * <p>This class is thread-safe.
 *
 * @author Chris Nokleberg
 * @since 1.0
 */

  if (!status->status.ok()) return nullptr;

  if (input_index >= op_def->input_arg_size() || input_index < 0) {
    status->status = tensorflow::errors::InvalidArgument(
        input_index, " out of range for ", op_name);
    return nullptr;
  }

  const tensorflow::OpDef_ArgDef& input_arg = op_def->input_arg()[input_index];

  if (input_arg.number_attr().empty()) {
    status->status = tensorflow::errors::NotFound(