"properties": {
          "field": {

        "properties": {
          "field": {

        "properties": {
          "field": {

        "properties": {
          "field": {

  auto variant_type = mlir::dyn_cast<mlir::TF::VariantType>(element_type);
  if (!variant_type) {
    return variant_params;
  }

  // We only support up to one nested type in tf_type.variant_type.
  if (variant_type.getSubtypes().size() > 1) {
    return std::nullopt;
  }
  if (variant_type.getSubtypes().empty()) {
    return variant_params;
  }

        "properties": {
          "field": {

  // (the versions field in graph.proto).
  bool InferShapeForSingleOperation(Operation* op, int64_t max_iterations);

  // Infers shape on the provided region, including nested ones, iterate until
  // fix point with a limit of max_iteration.
  // Returns a failure() on error, otherwise returns true to indicate that it
  // reached convergence, false otherwise.

  }
  func.return
  // expected-remark@above {{ID: 9}}
  // expected-remark@above {{Sinks: {8}}}
}

// -----

// Tests that the pass adds control dependencies in nested regions with
// tf_device.replicate

func.func @with_replicate(
  // expected-remark@above {{ID: 12}}
  %arg0: tensor<*x!tf_type.resource<tensor<32xf32>>>,
  %arg1: tensor<*x!tf_type.resource<tensor<32xf32>>>,

}

func (tr *TypeRequirement) goString(indent int, field string) string {
	return fmt.Sprintf("%*s%sTypeRequirement:\n%s", indent, "", field,
		tr.Type.goString(indent+2, ""))
}

// NestedRequirement is a nested requirement in a requires expression.
type NestedRequirement struct {
	Constraint AST
}

func (nr *NestedRequirement) print(ps *printState) {
	ps.writeString(" requires ")
	ps.print(nr.Constraint)

                         .dyn_cast_or_null<mlir::RankedTensorType>();
  if (input_type && weight_type && input_type.hasStaticShape() &&
      weight_type.hasStaticShape()) {
    // Compute op count from the seven nested loops of
    // tflite::reference_ops::TransposeConv():
    count = 2 * input_type.getNumElements() * weight_type.getDimSize(0) *
            weight_type.getDimSize(1) * weight_type.getDimSize(2);
  }