{"1.38.1", rel2, "go1.39.2"},
	{"1.38.1", relRC, "go1.39.2"},
	{"1.39", rel0, "go1.39.2"},
	{"1.39", rel2, "go1.39.2"},
	{"1.39", relRC, "go1.39.2"},
	{"1.39.2", rel0, "go1.39.2"},
	{"1.39.2", rel2, "go1.39.2"},
	{"1.39.2", relRC, "go1.39.2"},
	{"1.39.3", rel0, "go1.40.0"},
	{"1.39.3", rel2, "go1.40.2"},
	{"1.39.3", relRC, "go1.40.2"},
	{"1.40", rel0, "go1.40.0"},
	{"1.40", rel2, "go1.40.2"},

' ##################################

!define Rel_(e_alias1, e_alias2, e_label, e_direction="") e_alias1 e_direction e_alias2 : "===e_label"
!define Rel_(e_alias1, e_alias2, e_label, e_techn, e_direction="") e_alias1 e_direction e_alias2 : "===e_label\n//<size:TECHN_FONT_SIZE>[e_techn]</size>//"

!define Rel(e_from,e_to, e_label) Rel_(e_from,e_to, e_label, "-->")
!define Rel(e_from,e_to, e_label, e_techn) Rel_(e_from,e_to, e_label, e_techn, "-->")

  func.return %2 : tensor<4x8xf32>
}

//===----------------------------------------------------------------------===//
// Relu op legalizations.
//===----------------------------------------------------------------------===//

// -----

// CHECK-LABEL: func @relu
func.func @relu(%arg0: tensor<1xi32>) -> tensor<1xi32> {
  // CHECK: %[[ZERO:.*]] = mhlo.constant dense<0> : tensor<i32>

            // Others
            "AddN", "Bitcast", "Cast", "ClipByValue", "Const", "Empty",
            "Identity", "IdentityN", "Relu", "Relu6", "ReluGrad", "Relu6Grad",
            "LeakyReluGrad", "Elu", "EluGrad", "Selu", "SeluGrad", "Select",
            "SelectV2", "Transpose", "ConjugateTranspose",
            "_UnaryOpsComposition", "CollectiveReduceV2",
            "CollectiveAssignGroupV2",

// The actual Optimize Pass.
namespace {
#define GEN_PASS_DEF_OPTIMIZEPASS
#include "tensorflow/compiler/mlir/lite/transforms/passes.h.inc"

constexpr char kRelu[] = "RELU";
constexpr char kRelu6[] = "RELU6";
constexpr char kRelu1[] = "RELU_N1_TO_1";

ElementsAttr FlattenTo1D(Attribute a) {
  auto elements = mlir::cast<DenseElementsAttr>(a);

	//				0: R_LARCH_PCALA_HI20	local.moduledata
	o(0x1a000004)
	rel, _ := initfunc.AddRel(objabi.R_LOONG64_ADDR_HI)
	rel.SetOff(0)
	rel.SetSiz(4)
	rel.SetSym(ctxt.Moduledata)

	//	4:	02c00084	addi.d	$a0, $a0, 0
	//				4: R_LARCH_PCALA_LO12	local.moduledata
	o(0x02c00084)
	rel2, _ := initfunc.AddRel(objabi.R_LOONG64_ADDR_LO)
	rel2.SetOff(4)
	rel2.SetSiz(4)
	rel2.SetSym(ctxt.Moduledata)

	//	8:	50000000	b	0

// CHECK-LABEL:cos
// CHECK:  "tfl.cos"(%arg0) : (tensor<f32>) -> tensor<f32>
}

func.func @elu(%arg0: tensor<11x16xf32>) -> tensor<11x16xf32> {
  %0 = "tf.Elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32>
  func.return %0 : tensor<11x16xf32>

// CHECK-LABEL:elu
// CHECK:  "tfl.elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32>
}

 * <<#rel4.8:switch_to_publishing_plugins,Maven Publish and Ivy Publish Plugins>> that now support digital signatures with the <<signing_plugin#signing_plugin,Signing Plugin>>.
 * Use native <<#rel5.0:bom_import,BOM import>> in your builds.
 * The <<worker_api.adoc#worker_api,Worker API>> for enabling units of work to run in parallel.

				continue
			}
			val64 := sym.Value + uint64(rela.Addend)
			f.ByteOrder.PutUint64(dst[rela.Off:rela.Off+8], val64)
		case R_X86_64_32:
			if rela.Off+4 >= uint64(len(dst)) || rela.Addend < 0 {
				continue
			}
			val32 := uint32(sym.Value) + uint32(rela.Addend)
			f.ByteOrder.PutUint32(dst[rela.Off:rela.Off+4], val32)
		}
	}

	return nil
}

    "tf.Yield"(%t0, %t1, %t2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()
    }, {
     %e0 = "tf.Neg"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e1 = "tf.Relu"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     %e2 = "tf.Sin"(%arg1) : (tensor<2xf32>) -> tensor<2xf32>
     "tf.Yield"(%e0, %e1, %e2) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> ()