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) {

// CHECK-NEXT:      buffer: 2,
// CHECK-NEXT:      name: "outputs",
// CHECK-NEXT:      quantization: {
// CHECK:           }
// CHECK-NEXT:    } ],
// CHECK-NEXT:    inputs: [ 0 ],
// CHECK-NEXT:    outputs: [ 1 ],
// CHECK-NEXT:    operators: [ {
// CHECK-NEXT:      inputs: [ 0, 0 ],
// CHECK-NEXT:      outputs: [ 1 ],
// CHECK-NEXT:      builtin_options_type: AddOptions,
// CHECK-NEXT:      builtin_options: {
// CHECK:           }

     */
    SourceLocation sourceLocation();

    /**
     * Marks this closure as a rule action, with the given inputs
     *
     * @param nestedRuleFactory Factory to use to create nested rules.
     */
    void makeRule(PotentialInputs inputs, @Nullable ClosureBackedRuleFactory nestedRuleFactory);

}
Status TapeOperation::AddInput(AbstractTensorHandle* input) {
  TF_RETURN_IF_ERROR(parent_op_->AddInput(input));
  forward_op_.inputs.push_back(input);
  return absl::OkStatus();
}
Status TapeOperation::AddInputList(
    absl::Span<AbstractTensorHandle* const> inputs) {
  TF_RETURN_IF_ERROR(parent_op_->AddInputList(inputs));
  for (auto input : inputs) {
    forward_op_.inputs.push_back(input);
  }
  return absl::OkStatus();
}

Anis Eleuch <******@****.***> 1712232280 +0100

include::{samplesPath}/writing-tasks/tasks-with-dependency-resolution-result-inputs/tests/listResolvedArtifacts.out[]

BUILD SUCCESSFUL in 1s
1 actionable task: 1 executed
----
=====
=====
[.sample-command]
----
> ./gradlew -q graphResolvedComponents
include::{samplesPath}/writing-tasks/tasks-with-dependency-resolution-result-inputs/tests/graphResolvedComponents.out[]

BUILD SUCCESSFUL in 1s

Status LockVariables(absl::Span<VariableInfo> variables)
    TF_EXCLUSIVE_LOCK_FUNCTION();

// Returns a vector of VariableInfo instances for the resource variable inputs,
// given that *all* inputs are in `inputs`. The input indices for the resource
// variable inputs are in `variable_indices`.
//
// When using the VariableInfos generated by this version, all variables would
// be writer-locked.

// CHECK-NEXT:      name: "StatefulPartitionedCall:1",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:      },
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    } ],
// CHECK-NEXT:    inputs: [  ],
// CHECK-NEXT:    outputs: [ 0 ],
// CHECK-NEXT:    operators: [  ],
// CHECK-NEXT:    name: "main"
// CHECK-NEXT:  } ],
// CHECK-NEXT:  description: "MLIR Converted.",
// CHECK-NEXT:  buffers: [ {