user.Pets = append(user.Pets, &pet)
	CheckUser(t, user2, user)

	AssertAssociationCount(t, user, "Pets", 3, "AfterAppend")

	pets2 := []Pet{{Name: "pet-has-many-append-1-1"}, {Name: "pet-has-many-append-1-1"}}

	if err := DB.Model(&user2).Association("Pets").Append(&pets2); err != nil {
		t.Fatalf("Error happened when append pet, got %v", err)
	}

	for _, pet := range pets2 {
		pet := pet
		if pet.ID == 0 {

                                    <label for="doc.has_cache" class="col-sm-3 text-sm-right col-form-label">has_cache</label>
                                    <div class="col-sm-9">
                                        <la:errors property="doc.has_cache"/>
                                        <la:text styleId="doc.has_cache" property="doc.has_cache" styleClass="form-control"/>
                                    </div>

  DeviceNameUtils::ParsedName parsed_name;
  tensorflow::DeviceNameUtils::ParseFullName(device_name, &parsed_name);
  std::string device_type = parsed_name.type;
  int device_id = 0;
  if (parsed_name.has_id) {
    device_id = parsed_name.id;
  }

  ctx.device_id = device_id;
  if (device_type == "CPU") {
    ctx.device_type = DLDeviceType::kDLCPU;
  } else if (device_type == "GPU") {
#if TENSORFLOW_USE_ROCM

  bool has_tpu = false;
  for (int device_index = 0; device_index < TF_DeviceListCount(devices.get());
       ++device_index) {
    std::string device_type =
        TF_DeviceListType(devices.get(), device_index, status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
    if (device_type == "TPU") {
      has_tpu = true;
      break;
    }
  }

      maybe_parallel_results(
          parallel_device.Execute(context, parallel_inputs, operation_name,
                                  attributes, expected_max_outputs, status));
  if (!maybe_parallel_results.has_value()) return result;
  std::vector<std::unique_ptr<ParallelTensor>> parallel_results(
      std::move(maybe_parallel_results.value()));
  std::vector<MaybeParallelTensorOwned> result_content;

const (
	HasOne    RelationshipType = "has_one"      // HasOneRel has one relationship
	HasMany   RelationshipType = "has_many"     // HasManyRel has many relationship
	BelongsTo RelationshipType = "belongs_to"   // BelongsToRel belongs to relationship
	Many2Many RelationshipType = "many_to_many" // Many2ManyRel many to many relationship
	has       RelationshipType = "has"
)

type Relationships struct {

        TF_NewShapeAndTypeList(input_shapes_vec.size());
    for (size_t i = 0; i < input_shapes_vec.size(); ++i) {
      const auto& input_shape = input_shapes_vec[i];
      if (input_shape.has_value()) {
        TF_ShapeAndTypeListSetShape(input_shapes, i, input_shape->data(),
                                    input_shape->size());
      } else {
        TF_ShapeAndTypeListSetUnknownShape(input_shapes, i);

            Errors.RESOLUTION_TO_PRIVATE_CONSTRUCTOR_OF_SEALED_CLASS,
            Errors.UNRESOLVED_REFERENCE,
            *Errors.TYPECHECKER_HAS_RUN_INTO_RECURSIVE_PROBLEM.factories,
            *Errors.TYPECHECKER_HAS_RUN_INTO_RECURSIVE_PROBLEM_IN_AUGMENTED_ASSIGNMENT.factories,
        )

        private val syntaxErrors = setOf(
            Errors.ASSIGNMENT_IN_EXPRESSION_CONTEXT,
        )

  if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

  TFE_Execute(op_.get(), unwrapped_results.data(), &real_num_outputs, status);
  if (TF_GetCode(status) != TF_OK) {

index.field.url=url
index.field.doc_id=doc_id
index.field.id=_id
index.field.version=_version
index.field.seq_no=_seq_no
index.field.primary_term=_primary_term
index.field.lang=lang
index.field.has_cache=has_cache
index.field.last_modified=last_modified
index.field.anchor=anchor
index.field.segment=segment
index.field.role=role
index.field.boost=boost
index.field.created=created
index.field.timestamp=timestamp