* (unless there are {@link Double#NaN NaN} values, see below); otherwise, the result is the average
 * of the values which would appear at the indexes floor(x) and ceil(x) weighted by (1-frac(x)) and
 * frac(x) respectively. This is the same definition as used by Excel and by S, it is the Type 7
 * definition in <a

    int num_bits = qtype.getStorageTypeIntegralWidth();
    if (num_bits == 8) {
      // If storage is 8-bit, trained num bits may be less than 8 so check here.
      num_bits =
          static_cast<int>(std::ceil(std::log2(qtype.getStorageTypeMax())));
    }
    // This is a positive value, and will be applied on zero points and fixed
    // point ranges.
    int64_t offset =

}

func.func @ceil(%arg0: tensor<8x16xf32>) -> tensor<8x16xf32> {
  %0 = "tf.Ceil"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32>
  func.return %0 : tensor<8x16xf32>

// CHECK-LABEL: ceil
// CHECK:  "tfl.ceil"(%arg0) : (tensor<8x16xf32>) -> tensor<8x16xf32>
// CHECK:  return
}

 * (unless there are {@link Double#NaN NaN} values, see below); otherwise, the result is the average
 * of the values which would appear at the indexes floor(x) and ceil(x) weighted by (1-frac(x)) and
 * frac(x) respectively. This is the same definition as used by Excel and by S, it is the Type 7
 * definition in <a

  FILL = 94,
  FLOOR_MOD = 95,
  RANGE = 96,
  RESIZE_NEAREST_NEIGHBOR = 97,
  LEAKY_RELU = 98,
  SQUARED_DIFFERENCE = 99,
  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,

(Cvt64Fto32F ...) => (FRSP ...)

(CvtBoolToUint8 ...) => (Copy ...)

(Round(32|64)F ...) => (LoweredRound(32|64)F ...)

(Sqrt ...) => (FSQRT ...)
(Sqrt32 ...) => (FSQRTS ...)
(Floor ...) => (FFLOOR ...)
(Ceil ...) => (FCEIL ...)
(Trunc ...) => (FTRUNC ...)
(Round ...) => (FROUND ...)
(Copysign x y) => (FCPSGN y x)
(Abs ...) => (FABS ...)
(FMA ...) => (FMADD ...)

// Lowering extension

    auto normalized =
        rewriter.create<mhlo::DivOp>(op.getLoc(), abs_cast, delta_cast);
    auto ceil = rewriter.create<mhlo::CeilOp>(op.getLoc(), normalized);
    auto steps = rewriter.create<mhlo::ConvertOp>(
        op.getLoc(),
        tensorflow::GetTypeFromTFTensorShape({}, rewriter.getI64Type()), ceil);
    auto reshape = rewriter.create<mhlo::ReshapeOp>(
        op.getLoc(),

	}
}

// VersionLen is the Config Version and is only used as the nonce prefix, but we can reconstruct
// it because is is a b64 encoding of a 64 bit array, which will always be 12 chars in length.
// len = ceil(bitlength/(2^6))+1
const VersionLen = 12

func (s *DiscoveryServer) getResourceVersion(nonce, key string, cache map[string]string) string {
	if len(nonce) < VersionLen {
		return ""
	}

            roundFloorAsDouble = roundArbitrarily;
            roundFloor = roundArbitrarilyAsLong;
            roundCeilingAsDouble = Math.nextUp(roundArbitrarily);
            roundCeiling = (long) Math.ceil(roundCeilingAsDouble);
          } else {
            roundCeilingAsDouble = roundArbitrarily;
            roundCeiling = roundArbitrarilyAsLong;
            roundFloorAsDouble = DoubleUtils.nextDown(roundArbitrarily);

            roundFloorAsDouble = roundArbitrarily;
            roundFloor = roundArbitrarilyAsLong;
            roundCeilingAsDouble = Math.nextUp(roundArbitrarily);
            roundCeiling = (long) Math.ceil(roundCeilingAsDouble);
          } else {
            roundCeilingAsDouble = roundArbitrarily;
            roundCeiling = roundArbitrarilyAsLong;
            roundFloorAsDouble = DoubleUtils.nextDown(roundArbitrarily);