return in, out
}


func min(xs []uint64) uint64 {
	m := xs[0]
	for i := 1; i < len(xs); i++ {
		if xs[i] < m {
			m = xs[i]
		}
	}
	return m
}


func main() {
	F := []uint64{2, 3, 5}
	var n = len(F)
	OUT := []uint64{
		2, 3, 4, 5, 6, 8, 9, 10, 12, 15, 16, 18, 20, 24, 25, 27, 30, 32, 36,
		40, 45, 48, 50, 54, 60, 64, 72, 75, 80, 81, 90, 96, 100, 108, 120, 125,

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}>
  // CHECK: "tf.FusedBatchNormV3"(%arg0, {{.*}} <{data_format = "NCHW"
  // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%y, %[[RES_PERM]])
  // CHECK: return %[[RES_TRANSPOSE]]

  // Transpose NCHW -> NHWC
  %0 = "tf.Const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>

// CHECK-LABEL: func @transposeConv2D
func.func @transposeConv2D(%input: tensor<1x3x32x32xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32> {

  // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])

  // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1)
  // CHECK-SAME: data_format = "NHWC"

  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 8, 6, 7]
  // CHECK-SAME: (tensor<1x3x32x32xf32>, tensor<1x1x3x8xf32>) -> tensor<1x8x7x7xf32>

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%[[CONV2D]], %[[RES_PERM]])
  // CHECK: return %[[RES_TRANSPOSE]]

  %0 = "tf.Conv2D"(%input, %filter)
       {
         data_format = "NHWC",

00000040  00 00 00 00 00 00 00 00  00 00 00 00 00 32 cc a9  |.............2..|
00000050  cc a8 c0 2b c0 2f c0 2c  c0 30 c0 09 c0 13 c0 0a  |...+./.,.0......|
00000060  c0 14 00 9c 00 9d 00 2f  00 35 c0 12 00 0a c0 23  |......./.5.....#|
00000070  c0 27 00 3c c0 07 c0 11  00 05 13 03 13 01 13 02  |.'.<............|
00000080  01 00 00 79 00 0b 00 02  01 00 ff 01 00 01 00 00  |...y............|

Documentation = "https://fastapi.tiangolo.com/"
Repository = "https://github.com/tiangolo/fastapi"

[project.optional-dependencies]

standard = [
    "fastapi-cli >=0.0.2",
    # For the test client
    "httpx >=0.23.0",
    # For templates
    "jinja2 >=2.11.2",
    # For forms and file uploads
    "python-multipart >=0.0.7",
    # For UJSONResponse
    "ujson >=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0",

  return %2 : tensor<1x8x4x4xf32>
}
// CHECK-DAG: %[[CONST:.+]] = stablehlo.constant {{.*}} : tensor<3x3x8x8xf32>
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[ARG]], dims = [0, 2, 3, 1] : (tensor<1x8x4x4xf32>) -> tensor<1x4x4x8xf32>

#Activator-generated Properties
#Sun Apr 19 02:46:12 JST 2015
template.uuid=f875d6f6-609b-4be0-b365-b1c501866eeb

  const SmallVector<int64_t> permutation = {0, 2, 3, 1};

  const SmallVector<int> permutation_result = Permute<int>(arr, permutation);
  EXPECT_THAT(permutation_result, ElementsAre(0, 1, 2, 3));
}

TEST(PermutationTest, PermuteFourStringElements) {
  const SmallVector<std::string> arr = {"a", "b", "c", "d"};
  const SmallVector<int64_t> permutation = {0, 2, 3, 1};

  const SmallVector<std::string> permutation_result =

00000040  00 00 00 00 00 00 00 00  00 00 00 00 00 32 cc a9  |.............2..|
00000050  cc a8 c0 2b c0 2f c0 2c  c0 30 c0 09 c0 13 c0 0a  |...+./.,.0......|
00000060  c0 14 00 9c 00 9d 00 2f  00 35 c0 12 00 0a c0 23  |......./.5.....#|
00000070  c0 27 00 3c c0 07 c0 11  00 05 13 03 13 01 13 02  |.'.<............|
00000080  01 00 00 7f 00 0b 00 02  01 00 ff 01 00 01 00 00  |................|