}
	}
	t.Logf("For requiredSum=%v, %s classes=%#+v got solution %v, %v", requiredSum, style, classes, allocs, fairProp)
}

// partition64 calls consume n times, passing ints [0,n) and floats that sum to x
func partition64(rands *rand.Rand, n int, x float64, consume func(int, float64)) {
	if n <= 0 {
		return
	}
	divs := make([]float64, n-1)
	for idx := range divs {

      const std::function<bool(Operation *)> &partition = IsFlexDelegate)
      : partition_(partition) {}

  void runOnOperation() override;

 private:
  const std::function<bool(Operation *)> partition_;
};

void PartitionedTopologicalSortPass::runOnOperation() {
  func::FuncOp func = getOperation();
  if (!PartitionedTopologicalSort(func, &func.getBody().front(), partition_)) {
    return signalPassFailure();
  }

// RUN: odml-to-stablehlo-opt %s --tf-stablehlo=skip-partitioned-calls=true | FileCheck %s --check-prefix=CHECK-SKIP
// RUN: odml-to-stablehlo-opt %s --tf-stablehlo=skip-partitioned-calls=false | FileCheck %s --check-prefix=CHECK-NOSKIP

module {
  func.func @partitioned_call(%arg0: tensor<1x2x2x3xf32>) -> (tensor<1x2x2x3xf32>) {
    %0 = "tf.StatefulPartitionedCall"(%arg0) <{
      config = "", config_proto = "", executor_type = "", f = @some_func
    }> {

    List<List<Integer>> partitions = Lists.partition(source, 2);

    assertTrue(
        "partition should be RandomAccess, but not: " + partitions.getClass(),
        partitions instanceof RandomAccess);

    assertTrue(
        "partition[0] should be RandomAccess, but not: " + partitions.get(0).getClass(),
        partitions.get(0) instanceof RandomAccess);

    assertTrue(

    List<List<Integer>> partitions = Lists.partition(source, 2);

    assertTrue(
        "partition should be RandomAccess, but not: " + partitions.getClass(),
        partitions instanceof RandomAccess);

    assertTrue(
        "partition[0] should be RandomAccess, but not: " + partitions.get(0).getClass(),
        partitions.get(0) instanceof RandomAccess);

    assertTrue(

			pdqsort(data, mid+1, b, limit)
			b = mid
		}
	}
}

// partition does one quicksort partition.
// Let p = data[pivot]
// Moves elements in data[a:b] around, so that data[i]<p and data[j]>=p for i<newpivot and j>newpivot.
// On return, data[newpivot] = p
func partition(data Interface, a, b, pivot int) (newpivot int, alreadyPartitioned bool) {
	data.Swap(a, pivot)

// RUN: tf-opt %s -tfl-partitioned-topological-sort | FileCheck %s

// CHECK-LABEL: @tf_ops_will_be_partitioned
func.func @tf_ops_will_be_partitioned() -> tensor<1xf32> {
  %const = "tfl.pseudo_const"() {value = dense<[1.0]> : tensor<1xf32>} : () -> tensor<1xf32>
  %tmp1 = "tfl.add"(%const, %const) { fused_activation_function = "NONE" } : (tensor<1xf32>,tensor<1xf32>) -> (tensor<1xf32>)

		},
		{
			name: "with swap enabled, one partition",
			procSwapsContent: `
Filename				Type		Size		Used		Priority
/dev/dm-1               partition	33554428	0		-2
`,
			expectedEnabled: true,
		},
		{
			name: "with swap enabled, 2 partitions",
			procSwapsContent: `
Filename				Type		Size		Used		Priority
/dev/dm-1               partition	33554428	0		-2
/dev/zram0              partition	8388604		0		100
`,

}

// partitionEqual_func partitions data[a:b] into elements equal to data[pivot] followed by elements greater than data[pivot].
// It assumed that data[a:b] does not contain elements smaller than the data[pivot].
func partitionEqual_func(data lessSwap, a, b, pivot int) (newpivot int) {
	data.Swap(a, pivot)
	i, j := a+1, b-1 // i and j are inclusive of the elements remaining to be partitioned

	for {

			pdqsort{{.FuncSuffix}}(data, mid+1, b, limit {{.ExtraArg}})
			b = mid
		}
	}
}

// partition{{.FuncSuffix}} does one quicksort partition.
// Let p = data[pivot]
// Moves elements in data[a:b] around, so that data[i]<p and data[j]>=p for i<newpivot and j>newpivot.
// On return, data[newpivot] = p
func partition{{.FuncSuffix}}{{.TypeParam}}(data {{.DataType}}, a, b, pivot int {{.ExtraParam}}) (newpivot int, alreadyPartitioned bool) {