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"]} {

        (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;

                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 ],

// 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:             }

# See the License for the specific language governing permissions and
# limitations under the License.

INPUTS=("${@}")
TARGET_ARCH=${TARGET_ARCH:-amd64}
DOCKER_WORKING_DIR=${INPUTS[${#INPUTS[@]}-1]}
FILES=("${INPUTS[@]:0:${#INPUTS[@]}-1}")

set -eu;

# detect_arch returns "amd64", "arm64", or "" depending on if the file is arch specific
function detect_arch() {
  FILE=${1}

                             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);

    void people(ModelMap<Person> people) {
    }
}

apply plugin: PersonRules

// tag::configure-rule[]
// tag::create-rule[]
// tag::rule-inputs[]
// tag::create-rule-no-config[]
// tag::managed-type-nested-config-rule[]
model {
// end::create-rule[]
// end::rule-inputs[]
// end::create-rule-no-config[]
// end::managed-type-nested-config-rule[]
    person {
        lastName = "Smith"
    }
// end::configure-rule[]

                   absl::Span<AbstractTensorHandle* const> inputs,
                   absl::Span<AbstractTensorHandle*> outputs) {
  return ops::MatMul(ctx, inputs[0], inputs[1], &outputs[0],
                     /*transpose_a=*/false,
                     /*transpose_b=*/false, "MatMul");
}

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