func (f *Selector) Selects(inputs Set) bool {
	return !inputs.containsAny(f.absent) && inputs.containsAll(f.present)
}

// Excludes returns false, if the given set of labels, even combined with new ones, could end up satisfying the Selector.
// It returns false, if Matches would never return true, even if new labels are added to the input set.
func (f *Selector) Excludes(inputs Set) bool {
	return inputs.containsAny(f.absent)
}

namespace tensorflow {
namespace ops {

// Function that takes cond graph inputs and returns cond graph boolean output.
// 'output' need not be set if an error is returned.
typedef std::function<Status(const Scope&, const std::vector<Output>& inputs,
                             Output* output)>
    CondGraphBuilderFn;

// Function that takes body graph inputs and returns body graph outputs.

                this.inputs = ImmutableList.copyOf(inputs);
                this.action = action;
            }

            @Override
            public void execute(MutableModelNode modelNode, List<ModelView<?>> inputs) {
                action.execute(modelNode, inputs);
            }

            @Override
            public List<? extends ModelReference<?>> getInputs() {
                return inputs;
            }

                final boolean supportsNestedRules = node.canBeViewedAs(MANAGED_INSTANCE_TYPE);
                InputReferences inputs = transformedClosure.inputReferences();
                List<InputReference> inputReferences = supportsNestedRules ? inputs.getOwnReferences() : inputs.getAllReferences();
                final Map<String, PotentialInput> inputValues = new LinkedHashMap<>();

  // CHECK-NEXT:       has_rank: true
  // CHECK-NEXT:     } ],
  // CHECK-NEXT:     inputs: [ 0 ],
  // CHECK-NEXT:     outputs: [ 6 ],
  // CHECK-NEXT:     operators: [ {
  // CHECK-NEXT:       inputs: [ 0, 1 ],
  // CHECK-NEXT:       outputs: [ 2 ]
  // CHECK-NEXT:     }, {
  // CHECK-NEXT:       opcode_index: 1,
  // CHECK-NEXT:       inputs: [ 0, 2 ],
  // CHECK-NEXT:       outputs: [ 3 ],

        (MHLO_CompositeOp:$composite
          (variadic $inputs),
          ConstantStrAttr<StrAttr, "aten.gelu.default">, $attrs, $_, $_),
        (TFL_GeluOp $inputs, ConstBoolAttrFalse),
        [(IsStrCompositeAttribute<"approximate", "none"> $attrs)]>;

def LegalizeCompositeApproximateAtenGELU : Pat<
        (MHLO_CompositeOp:$composite
          (variadic $inputs),

Status ReluModel(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> inputs,
                 absl::Span<AbstractTensorHandle*> outputs) {
  return ops::Relu(ctx, inputs[0], &outputs[0], "Relu");
}

Status SparseSoftmaxCrossEntropyWithLogitsModel(
    AbstractContext* ctx, absl::Span<AbstractTensorHandle* const> inputs,
    absl::Span<AbstractTensorHandle*> outputs) {
  AbstractTensorHandle* loss;

  func.func @add(%arg0: tensor<?xf32> {tf_saved_model.index_path = ["input1"]}, %arg1: tensor<?xf32> {tf_saved_model.index_path = ["input2"]}) -> (tensor<?xf32> {tf_saved_model.index_path = ["result"]}) attributes {tf.entry_function = {control_outputs = "", inputs = "input1:0,input2:0", outputs = "result:0"}, tf_saved_model.exported_names = ["add"]} {

// CHECK:             quantization: {
// CHECK:             },
// CHECK:             has_rank: true
// CHECK:           } ],
// CHECK:           inputs: [ 0 ],
// CHECK:           outputs: [ 2 ],
// CHECK:           operators: [ {
// CHECK:             inputs: [ 1, 0 ],
// CHECK:             outputs: [ 2 ],
// CHECK:             builtin_options_type: AddOptions,
// CHECK:             builtin_options: {
// CHECK:             }

                             absl::Span<AbstractTensorHandle* const> inputs,
                             std::vector<AbstractTensorHandle*>* params) {
  tracing::TracingTensorHandle* handle = nullptr;
  for (auto input : inputs) {
    PartialTensorShape shape;
    TF_RETURN_IF_ERROR(input->Shape(&shape));
    TF_RETURN_IF_ERROR(dyn_cast<tracing::TracingContext>(ctx)->AddParameter(
        input->DataType(), shape, &handle));
    params->emplace_back(handle);