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

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

	}

	found := false
	returnTypes := expressionAccessor.ReturnTypes()
	for _, returnType := range returnTypes {
		if ast.OutputType() == returnType || cel.AnyType == returnType {
			found = true
			break
		}
	}
	if !found {
		var reason string
		if len(returnTypes) == 1 {
			reason = fmt.Sprintf("must evaluate to %v", returnTypes[0].String())
		} else {

            .configure(ModelActionRole.Initialize) { it.path("foo").type(Bean).action(action) }
            .configure(ModelActionRole.Mutate) { it.path("foo").type(Bean).action(action) }
            .configure(ModelActionRole.Finalize) { it.path("foo").type(Bean).action(action) }
            .configure(ModelActionRole.Validate) { it.path("foo").type(Bean).action(action) }

        when:

        1. Set _name = "MatMul"
        2. For each TF attribute, split it into two attributes, one for name of
           the TF attribute, and the other for the type and value of the
           attribute value. Attribute value is a string with the format of
           "type$val", where type can be "bool", "string", "tfdtype", "tfshape",
           "tftensor".
           The value serialization format can be found in attr_util.h.

func (v *ExtraMappingExpression) ReturnTypes() []*celgo.Type {
	// return types is only used for validation. The claims variable that's available
	// to the claim mapping expressions is a map[string]interface{}, so we can't
	// really know what the return type is during compilation. Strict type checking
	// is done during evaluation.
	return []*celgo.Type{celgo.AnyType}
}

var _ ExpressionAccessor = &UserValidationCondition{}

)

// 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 {

              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
              )
          ]

                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);

			// underlying; if the underlying type is unrestricted there must be an
			// element of the type set that violates one of the arg type checks
			// below, so we can safely return false here.

			if m.t == anyType { // anyType must have already been handled.
				panic("unexpected printfArgType")
			}
			return false
		}

		// Only report a reason if typ is the argument type, otherwise it won't

  @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),
        }