// first k fields of t1 equals the first k fields of t2.
// SameSizeTuplePrefixComparator(k1, k2) would be a compiler error if
// k1 != k2.
template <int kSize1, int kSize2>
struct SameSizeTuplePrefixComparator;

template <>
struct SameSizeTuplePrefixComparator<0, 0> {
  template <class Tuple1, class Tuple2>
  static bool Eq(const Tuple1& /* t1 */, const Tuple2& /* t2 */) {

// first k fields of t1 equals the first k fields of t2.
// SameSizeTuplePrefixComparator(k1, k2) would be a compiler error if
// k1 != k2.
template <int kSize1, int kSize2>
struct SameSizeTuplePrefixComparator;

template <>
struct SameSizeTuplePrefixComparator<0, 0> {
  template <class Tuple1, class Tuple2>
  static bool Eq(const Tuple1& /* t1 */, const Tuple2& /* t2 */) {

def LegalizeAveragePool : Pat<(TF_AvgPoolOp $value,
              IsIntList1XY1:$ksize,
              IsIntList1XY1:$strides,
              $padding,
              IsDataFormatNHWC:$format),
          (TFL_AveragePool2DOp $value,
              /*filter_height=*/ExtractI32At<1>:$ksize,
              /*filter_width=*/ExtractI32At<2>:$ksize,
              /*padding=*/$padding,
              /*stride_h=*/ExtractI32At<1>:$strides,

  %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %2 = "tf.MaxPool"(%1) {data_format = "NHWC", device = "", explicit_paddings = [], ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]} : (tensor<*xf32>) -> tensor<*xf32>
  %3 = "quantfork.qcast"(%2) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>

	var dum [4]byte
	size1 := 0
	for _, r := range a {
		size1 += encoderune(dum[:], r)
	}
	s, b := rawstringtmp(buf, size1+3)
	size2 := 0
	for _, r := range a {
		// check for race
		if size2 >= size1 {
			break
		}
		size2 += encoderune(b[size2:], r)
	}
	return s[:size2]
}

type stringStruct struct {
	str unsafe.Pointer
	len int
}

  %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %2 = "tf.MaxPool"(%1) {data_format = "NHWC", device = "", explicit_paddings = [], ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]} : (tensor<*xf32>) -> tensor<*xf32>
  %3 = "quantfork.qcast"(%2) {volatile} : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>

      CompileTimeConstantOperand<TF::MaxOp, 1>,  // $reduction_indices
      // $ksize, $strides
      CompileTimeConstantOperand<TF::MaxPoolGradGradV2Op, 3, 4>,
      // $ksize, $strides
      CompileTimeConstantOperand<TF::MaxPoolGradV2Op, 2, 3>,
      CompileTimeConstantOperand<TF::MaxPoolV2Op, 1, 2>,   // $ksize, $strides
      CompileTimeConstantOperand<TF::MeanOp, 1>,           // $reduction_indices

          rewriter.getI64ArrayAttr(kstrides), rewriter.getStringAttr(padding),
          rewriter.getStringAttr(data_format));
      return success();
    } else if (ksizes.size() == 5) {
      rewriter.replaceOpWithNewOp<TF::AvgPool3DOp>(
          op, op.getType(), input, rewriter.getI64ArrayAttr(ksizes),
          rewriter.getI64ArrayAttr(kstrides), rewriter.getStringAttr(padding),

  // OK
  %0 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  // Unsupported ksize
  %1 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [3, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 1]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>

      %outputs_14, %control_15 = tf_executor.island wraps "tf.AvgPool"(%outputs_12) {data_format = "NHWC", device = "", ksize = [1, 7, 7, 1], padding = "VALID", strides = [1, 7, 7, 1]} : (tensor<16x112x112x?xf32>) -> tensor<16x16x16x?xf32>