// following condition must hold true for padding to be `SAME`:
    // output_spatial_shape[i] = ceil(input_spatial_shape[i] / strides[i])
    auto get_output_dim_for_same_padding = [](int64_t input_dim,
                                              int64_t stride_dim) -> int64_t {
      return std::ceil(input_dim / static_cast<double>(stride_dim));
    };
    return output_height ==

}

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
}

    }], [{
      TypeAttr::get(getResult().getType().cast<TensorType>().getElementType())
    }]>;
}

def TFL_CeilOp: TFL_Op<"ceil", [
    Pure,
    TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Ceil operator";

  let description = [{
    Returns element-wise ceil value of the input.
  }];

  let arguments = (ins TFL_FpTensor:$x);

  let results = (outs TFL_FpTensor:$y);

(Select1 (Div128u (Const64 [0]) lo y)) => (Mod64u lo y)

(Not (ConstBool [c])) => (ConstBool [!c])

(Floor       (Const64F [c])) => (Const64F [math.Floor(c)])
(Ceil        (Const64F [c])) => (Const64F [math.Ceil(c)])
(Trunc       (Const64F [c])) => (Const64F [math.Trunc(c)])
(RoundToEven (Const64F [c])) => (Const64F [math.RoundToEven(c)])

// Convert x * 1 to x.

            "format": "int32",...

            "format": "int32",...

  // tensorflow/lite/kernels/range.cc.
  FloatOrInt diff = limit - start;
  if (std::is_integral<FloatOrInt>::value) {
    return ((std::abs(diff) + std::abs(delta) - 1) / std::abs(delta));
  }
  return std::ceil(std::abs(diff / delta));
}

// Builds a constant range tensor of `result_elem_type` elements.
// Template parameter `FloatOrIntAtrr` must be mlir::IntegerAttr or
// mlir::FloatAttr.
template <typename FloatOrIntAtrr>

	resourcesUsedRatio := float64(totalUsedCPUOfAllPods) / float64(float64(totalRequestedCPUOfAllPods)*target)

	// i.e. .60 * 20 -> scaled down expectation.
	finalPods := int32(math.Ceil(resourcesUsedRatio * float64(startPods)))

	// To breach tolerance we will create a utilization ratio difference of tolerance to usageRatioToleranceValue)
	tc1 := testCase{
		minReplicas:             0,

  // tensorflow/lite/kernels/range.cc.
  return std::is_integral<FloatOrInt>::value
             ? ((std::abs(limit - start) + std::abs(delta) - 1) /
                std::abs(delta))
             : std::ceil(std::abs((limit - start) / delta));
}

// Builds a constant range tensor of `result_elem_type` elements.
// Template parameter `FloatOrIntAtrr` must be mlir::IntegerAttr or
// mlir::FloatAttr.

(Neg(64|32|16|8) ...) => (NEG ...)
(Neg(32|64)F     ...) => (FNEG(S|D) ...)
(Com(64|32|16|8) ...) => (MVN ...)

// math package intrinsics
(Abs         ...) => (FABSD   ...)
(Sqrt        ...) => (FSQRTD  ...)
(Ceil        ...) => (FRINTPD ...)
(Floor       ...) => (FRINTMD ...)
(Round       ...) => (FRINTAD ...)
(RoundToEven ...) => (FRINTND ...)
(Trunc       ...) => (FRINTZD ...)
(FMA       x y z) => (FMADDD z x y)