};

    ValueRange opInput = op.getInput();
    TypeRange scf_block_arguments_type = opInput.getType();

    // Create the `scf.while` op.
    auto scf_while_op = rewriter.create<scf::WhileOp>(
        op.getLoc(), op.getResultTypes(), opInput);

    // Create the `before` block of the `scf.while` op (with an `scf.condition`
    // op as the terminator). Note that the arguments' type of this block is

GradientFunction* AddRegisterer(const ForwardOperation& op);
GradientFunction* ExpRegisterer(const ForwardOperation& op);
GradientFunction* MatMulRegisterer(const ForwardOperation& op);
GradientFunction* SqrtRegisterer(const ForwardOperation& op);
GradientFunction* NegRegisterer(const ForwardOperation& op);
GradientFunction* SubRegisterer(const ForwardOperation& op);
GradientFunction* MulRegisterer(const ForwardOperation& op);

  op->walk([&](Operation *op) {
    if (op->getDialect()->getNamespace() != "vhlo") return;

    // Convert operands
    rewriter.modifyOpInPlace(op, [&]() {
      rewriter.setInsertionPoint(op);
      WrapOperandsInUnrealizedCastAndConvert(op, converter, rewriter);

      // Convert op types
      for (auto value : op->getResults()) {
        rewriter.setInsertionPointAfter(value.getDefiningOp());

	for i.Op == InstNop || i.Op == InstCapture {
		i = &p.Inst[i.Out]
	}
	return i
}

// op returns i.Op but merges all the Rune special cases into InstRune
func (i *Inst) op() InstOp {
	op := i.Op
	switch op {
	case InstRune1, InstRuneAny, InstRuneAnyNotNL:
		op = InstRune
	}
	return op
}

// Prefix returns a literal string that all matches for the

      // requantize op to remove the unnecessary requantize op.
      if (const QuantizedType qtype =
              QuantizedType::getQuantizedElementType(q.getArg().getType())) {
        rewriter.setInsertionPoint(op);
        rewriter.replaceOpWithNewOp<quantfork::DequantizeCastOp>(
            op, op.getResult().getType(), q.getArg());
        return success();
      }

      op.replaceAllUsesWith(q.getArg());

	v := a[0]
	if v.Op == shiftOp {
		v = v.Args[0]
	}
	var extOp Op
	if orOp == OpOr64 && (v.Op == OpZeroExt8to64 || v.Op == OpZeroExt16to64 || v.Op == OpZeroExt32to64) ||
		orOp == OpOr32 && (v.Op == OpZeroExt8to32 || v.Op == OpZeroExt16to32) ||
		orOp == OpOr16 && v.Op == OpZeroExt8to16 {
		extOp = v.Op
		v = v.Args[0]
	} else {
		return false
	}
	if v.Op != OpLoad {
		return false
	}

                  TF_Status* s, const char* name) {
  TF_Operation* op;
  AddOpHelper(l, r, graph, s, name, &op, true);
  return op;
}

TF_Operation* AddNoCheck(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                         TF_Status* s, const char* name) {
  TF_Operation* op;
  AddOpHelper(l, r, graph, s, name, &op, false);
  return op;
}

LogicalResult ShapeNOp::verify() {
  ShapeNOp op = *this;
  const size_t num_tensors = op.getN();

  if (op.getNumOperands() != num_tensors)
    return op.emitOpError() << "requires " << num_tensors << " operand(s), got "
                            << op.getNumOperands() << " operand(s)";

  if (op.getNumResults() != num_tensors)
    return op.emitOpError() << "requires " << num_tensors << " result(s), got "

//===----------------------------------------------------------------------===//
// DivNoNan and MulNonNan op patterns.
//===----------------------------------------------------------------------===//

class BinaryNoNanPat<Op FromOp, Op ToOp>
  : Pat<(FromOp $l, $r),
        (TF_SelectV2Op (TF_EqualOp $r,
                                   (TF_ConstOp:$zero (GetScalarOfType<0> $r)),

node {
  name: "tf.Less"
  op: "Less"
  input: "a"
  input: "b"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  experimental_debug_info {
  }
}
node {
  name: "my_equal"
  op: "Equal"
  input: "a"
  input: "b"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }