v.Op = OpMIPS64SUBV
		return true
	case OpSub64F:
		v.Op = OpMIPS64SUBD
		return true
	case OpSub8:
		v.Op = OpMIPS64SUBV
		return true
	case OpSubPtr:
		v.Op = OpMIPS64SUBV
		return true
	case OpTailCall:
		v.Op = OpMIPS64CALLtail
		return true
	case OpTrunc16to8:
		v.Op = OpCopy
		return true
	case OpTrunc32to16:
		v.Op = OpCopy
		return true
	case OpTrunc32to8:
		v.Op = OpCopy

		v.Op = OpCopy
		return true
	case OpTrunc64to16:
		v.Op = OpCopy
		return true
	case OpTrunc64to32:
		v.Op = OpCopy
		return true
	case OpTrunc64to8:
		v.Op = OpCopy
		return true
	case OpWB:
		v.Op = OpRISCV64LoweredWB
		return true
	case OpXor16:
		v.Op = OpRISCV64XOR
		return true
	case OpXor32:
		v.Op = OpRISCV64XOR
		return true
	case OpXor64:
		v.Op = OpRISCV64XOR

(Add64 (Add64 i:(Const64 <t>) z) x) && (z.Op != OpConst64 && x.Op != OpConst64) => (Add64 i (Add64 <t> z x))
(Add32 (Add32 i:(Const32 <t>) z) x) && (z.Op != OpConst32 && x.Op != OpConst32) => (Add32 i (Add32 <t> z x))
(Add16 (Add16 i:(Const16 <t>) z) x) && (z.Op != OpConst16 && x.Op != OpConst16) => (Add16 i (Add16 <t> z x))
(Add8  (Add8  i:(Const8  <t>) z) x) && (z.Op != OpConst8  && x.Op != OpConst8)  => (Add8  i (Add8  <t> z x))

    }

    // CustomTfOp is just a wrapper around a TF op, we export the custom Op
    // not the wrapper, so we fetch the op from the region.
    if (auto custom_op = dyn_cast<mlir::TFL::CustomTfOp>(inst)) {
      // If we have custom op with a region, then use the first op in the
      // region, if it exists, otherwise just use params for custom op.
      if (!custom_op.getBody().empty()) {

		args[2+i] = a.Class - 1
	}

	// Lookup op in optab.
	ops := oprange[p.As&obj.AMask]
	cmp := [len(args)]*[C_NCLASS]bool{}
	for i := range cmp {
		cmp[i] = &xcmp[args[i]]
	}
	for i := range ops {
		op := &ops[i]
		if cmp[0][op.a1] && cmp[1][op.a2] &&
			cmp[2][op.a3] && cmp[3][op.a4] &&
			cmp[4][op.a5] && cmp[5][op.a6] {
			p.Optab = uint16(cap(optab) - cap(ops) + i + 1)
			return op

    auto shape_constant = rewriter.create<ConstOp>(
        op.getLoc(), GetI32ElementsAttr(new_shape, &rewriter));
    auto new_reshape = rewriter.create<ReshapeOp>(
        op.getLoc(), transpose_op.getInput(), shape_constant);
    rewriter.replaceOpWithNewOp<BatchMatMulOp>(
        op, op.getType(), op.getX(), new_reshape, op.getAdjX(), !op.getAdjY(),
        op.getAsymmetricQuantizeInputsAttr());
    return success();
  }

// Tests for TensorFlow ops with custom verifiers.

// TODO(hinsu): Remove tests for ops without custom verifiers. These tests were
// added along with manual op definition and are obsolete now that the op
// definitions are auto-generated.

// TODO(hinsu): Move attribute and type tests to types.mlir file.
//===--------------------------------------------------------------------===//

				ab.asmvex(ctxt, &p.From, p.GetFrom3(), &p.To, o.op[z], o.op[z+1])
				ab.asmando(ctxt, cursym, p, &p.From, int(o.op[z+2]))

			case Zvex_i_rm_vo:
				ab.asmvex(ctxt, p.GetFrom3(), &p.To, nil, o.op[z], o.op[z+1])
				ab.asmando(ctxt, cursym, p, p.GetFrom3(), int(o.op[z+2]))
				ab.Put1(byte(p.From.Offset))

			case Zvex_i_r_v:
				ab.asmvex(ctxt, p.GetFrom3(), &p.To, nil, o.op[z], o.op[z+1])
				regnum := byte(0x7)

  // expected-error @+1 {{op failed to verify that params and output must have same element type}}
  %0 = "tfl.gather"(%arg0, %arg1) {axis = 1 : i32}: (tensor<?xi32>,tensor<?xi32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

// -----

func.func @testGatherUnsupportedRank(%arg0 : tensor<f32>, %arg1 : tensor<1xi32>) -> tensor<?xf32> {
  // expected-error @+1 {{op failed to verify that operand 0 is 1-D}}

  let summary = "Wrapper Op for TF custom ops.";

  let description = [{
    A wrapper op around any Custom TF op. These includes ops defined using
    custom_opdefs or linked which are not defined in TF dialect.
    This Op just wraps the custom op inside a region.
    Note #1, this Op will not include TF Lite custom ops defined using CustomOp.
    Note #2, this op is just internal representation inside the converter and