encoder.writeSmallInt(-1);
                return;
            }
            Class<?> anyType = value.getClass();
            Serializer<Object> serializer = Cast.uncheckedCast(SERIALIZER_FACTORY.getSerializerFor(anyType));
            for (int i = 0; i < USUAL_TYPES.length; i++) {
                if (USUAL_TYPES[i].equals(anyType)) {
                    encoder.writeSmallInt(i);
                    serializer.write(encoder, value);

        Type parameterType = extractType(parameterElement.asType());
        ParameterKindInfo parameterKindInfo = extractParameterKind(parameterElement, isVararg);

        if (parameterKindInfo == ParameterKindInfo.VARARG_METHOD_PARAMETER) {
            if (parameterType.getSort() != Type.ARRAY) {

)

// SigningCAFileBundle locations of the files used for the signing CA
type SigningCAFileBundle struct {
	RootCertFile    string
	CertChainFiles  []string
	SigningCertFile string
	SigningKeyFile  string
}

var pkiCaLog = log.RegisterScope("pkica", "Citadel CA log")

// caTypes is the enum for the CA type.
type caTypes int

type CertOpts struct {

                return FILE_COLLECTION.asType();
            case DIRECTORY_PROPERTY:
            case REGULAR_FILE_PROPERTY:
                return Type.getType(File.class);
            case LIST_PROPERTY:
                return Type.getType(List.class);
            case SET_PROPERTY:
                return Type.getType(Set.class);
            case MAP_PROPERTY:
                return Type.getType(Map.class);

  @test_util.deprecated_graph_mode_only
  def test_replace_tensors_by_numpy_ndarrays_with_tensor_list(self):
    num_samples = 8
    samples = [
        np.random.uniform(low=-1.0, high=1.0, size=(3, 3)).astype('f4')
        for _ in range(num_samples)
    ]

    repr_ds: repr_dataset.RepresentativeDataset = [
        {
            'input_tensor': ops.convert_to_tensor(sample),
        }

              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[
              tensor_spec.TensorSpec(
                  name='x', shape=x_signature, dtype=dtypes.float32
              )
          ]

		config = &defaultConfig
	}

	x, xType, ok := functionAndType(f)
	if !ok {
		return SetupError("f is not a function")
	}
	y, yType, ok := functionAndType(g)
	if !ok {
		return SetupError("g is not a function")
	}

	if xType != yType {
		return SetupError("functions have different types")
	}

	arguments := make([]reflect.Value, xType.NumIn())
	rand := config.getRand()

            default:
                throw new UnsupportedOperationException("Generating get call for type: " + upgradedPropertyType.asType() + " is not supported");
        }
    }

    private static CodeBlock getSetCall(String propertyGetterName, PropertyUpgradeRequestExtra implementationExtra, GradleLazyType upgradedPropertyType) {

        this.keyType = keyType;
        this.valueType = valueType;
        keyCollector = new ValidatingValueCollector<>(Set.class, keyType, ValueSanitizers.forType(keyType));
        entryCollector = new ValidatingMapEntryCollector<>(keyType, valueType, ValueSanitizers.forType(keyType), ValueSanitizers.forType(valueType));
        init();
    }

    private void init() {
        defaultValue = emptySupplier();

}

// A StorageCast (scast) represents a cast from or to a type based on the
// storage type and a type based on a corresponding quantized type.
//
// This op exists to ensure type coherency for between parts of the computation
// which are operating directly on an underlying storage type and those which
// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">