minReg := 14 // _savegpr0_{n}, _savegpr1_{n}, _savefpr_{n}, 14 <= n <= 31
	offMul := 4  // 1 instruction per register op.
	switch s[1] {
	case "savegpr0", "savegpr1", "savefpr":
	case "restgpr0", "restgpr1", "restfpr":
	case "savevr", "restvr":
		minReg = 20 // _savevr_{n} or _restvr_{n}, 20 <= n <= 31
		offMul = 8  // 2 instructions per register op.
	default:
		return 0, false // Not an abi func
	}
	n, e := strconv.Atoi(s[2])

	mc := o.newMetacache()
	meta := metaCacheRPC{meta: &mc, cancel: cancel, rpc: globalNotificationSys.restClientFromHash(pathJoin(o.Bucket, o.Prefix)), o: *o}

	// Save listing...
	go func() {
		if err := saver.saveMetaCacheStream(listCtx, &meta, saveCh); err != nil {
			meta.setErr(err.Error())
		}
		cancel()
	}()

	// Do listing...
	go func(o listPathOptions) {
		err := z.listMerged(listCtx, o, inCh)

// CHECK: "tf.SaveV2"(%[[ARG]], %[[CONST_0]], %[[CONST_1]], %[[READ_VARIABLE_0]], %[[READ_VARIABLE_1]])
// CHECK: return
}

// -----


// SaveV2 op not created when SessionInitializerOp doesn't exist.

module attributes {tf_saved_model.semantics} {
// CHECK-NOT: @tf_quant__save
}

// -----

// SaveV2 op not created when there are no VarHandleOp in the session

    init_op_name: Name of the node for initialization.
    saver_def: `saver_pb2.SaverDef` to create a `saver.Saver` from. The created
      saver will be used to save and load variables. This may be `None` if no
      variables exist in the graph.
    checkpoint_dir: Path to checkpoint file where variable values are saved.
    function_aliases: Function name -> function alias mapping.

    return "Inserts a new function that wraps a SaveV2 op. The SaveV2 op saves "
           "the values of the VarHandleOps that are found in the initializer "
           "function of 'restore_op' type.";
  }

  void runOnOperation() override;
};

// Finds `tf.AssignVariableOp(tf.VarHandleOp, tf.Const)` patterns and removes
// `tf.AssignVariableOp`s and `tf.Const`s. Collects and returns the

// CHECK: %[[CTL_0:.*]] = tf_executor.island wraps "tf.SaveV2"(%[[ARG_0]], %[[CST_0]], %[[CST_1]], %[[READ_VARIABLE]]) : (tensor<!tf_type.string>, tensor<1x!tf_type.string>, tensor<1x!tf_type.string>, tensor<2xf32>) -> ()

// Test that the Identity op has been created to fetch the file prefix
// argument. It should also have control dependency to the `SaveV2` op.

  """Test cases for RepresentativeDatasetSaver."""

  def test_save_raises_error(self):
    saver = repr_dataset.RepresentativeDatasetSaver()
    repr_ds = {'serving_default': []}

    with self.assertRaisesRegex(
        NotImplementedError, 'Method "save" is not implemented.'
    ):
      saver.save(repr_ds)


class TfRecordRepresentativeDatasetTest(test.TestCase):

	// g (R30) and REGTMP (R23)  might be clobbered by load_g. They
	// are callee-save in the gcc calling convention, so save them.
	MOVV	R23, savedR23-16(SP)
	MOVV	g, savedG-8(SP)

	JAL	runtime·load_g(SB)
	MOVV	g_m(g), R1
	MOVV	m_curg(R1), R1
	MOVV	(g_stack+stack_hi)(R1), R2 // return value in R2

	MOVV	savedG-8(SP), g
	MOVV	savedR23-16(SP), R23
	RET

// The top-most function running on a goroutine

limitations under the License.
==============================================================================*/
#include "tensorflow/cc/experimental/libexport/save.h"

#include "tensorflow/core/platform/env.h"

namespace tensorflow {
namespace libexport {

Status Save(const std::string& export_dir) {
  TF_RETURN_IF_ERROR(Env::Default()->RecursivelyCreateDir(export_dir));
  return absl::OkStatus();
}

}  // namespace libexport

	MOVV	R11, 88(R3)
	MOVV	R12, 96(R3)
	// R13 already saved
	MOVV	R14, 104(R3)
	MOVV	R15, 112(R3)
	MOVV	R16, 120(R3)
	MOVV	R17, 128(R3)
	MOVV	R18, 136(R3)
	// R19 already saved
	MOVV	R20, 144(R3)
	MOVV	R21, 152(R3)
	// R22 is g.
	MOVV	R23, 160(R3)
	MOVV	R24, 168(R3)
	MOVV	R25, 176(R3)
	MOVV	R26, 184(R3)
	// R27 already saved
	// R28 already saved.
	MOVV	R29, 192(R3)
	// R30 is tmp register.