// CHECK-OPT: %{{.*}} = stablehlo.custom_call @tf.ResizeBilinear(%arg0, %cst) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<1x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>
      %1 = "tf.ResizeBilinear"(%arg0, %0) {

  func.return %2 : tensor<1x32x32x16xf32>
}

// -----

// CHECK-LABEL: @test_conv3d_float
func.func @test_conv3d_float(%arg0: tensor<1x32x32x32x8xf32>) -> tensor<1x32x32x32x16xf32> {

  %w = arith.constant dense<127.0> : tensor<1x32x42x128xf32>
  %b = arith.constant dense<0.0> : tensor<1x32x42x128xf32>
  %tconv = "tfl.transpose_conv"(%arg1, %w, %arg0, %b) {padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32, fused_activation_function = "NONE"} : (tensor<4xi32>, tensor<1x32x42x128xf32>, tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>) -> tensor<1x32x42x128xf32>

    "tfl.yield"(%4, %5, %6, %7) : (tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>) -> ()
  }) : (tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>, tensor<1x64x64x32xf32>) -> (tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>, tensor<1x32x32x32xf32>)

func.func @main(%arg0: tensor<32x4x4x128xf32>, %arg1: tensor<1x32x42x128xf32>, %arg2: tensor<4xi32>) -> tensor<1x64x84x32xf32> {
  %0 = "tfl.custom"(%arg0, %arg1, %arg2) {custom_code = "Convolution2DTransposeBias", custom_option = #tfl<const_bytes : "0x010000000200000002000000">} : (tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, tensor<4xi32>) -> tensor<1x64x84x32xf32>
  func.return %0 : tensor<1x64x84x32xf32>
}

func.func @main(%arg0: tensor<32x4x4x128xf32>, %arg1: tensor<1x32x42x128xf32>, %arg2: tensor<4xi32>) -> tensor<1x64x84x32xf32> {
  %0 = "tfl.custom"(%arg0, %arg1, %arg2) {custom_code = "Convolution2DTransposeBias", custom_option = #tfl<const_bytes : "0x010000000200000002000000">} : (tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>, tensor<4xi32>) -> tensor<1x64x84x32xf32>
  func.return %0 : tensor<1x64x84x32xf32>
}

  %w = arith.constant dense<127.0> : tensor<1x32x42x128xf32>
  %b = arith.constant dense<0.0> : tensor<1x32x42x128xf32>
  %tc = "tfl.transpose_conv"(%arg1, %w, %arg0, %b) {padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32, fused_activation_function = "NONE"} : (tensor<4xi32>, tensor<1x32x42x128xf32>, tensor<32x4x4x128xf32>, tensor<1x32x42x128xf32>) -> tensor<1x32x42x128xf32>

  %tconv_s = "quantfork.stats"(%tconv) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<1x32x42x128xf32>) -> tensor<1x32x42x128xf32>
  func.return %tconv_s : tensor<1x32x42x128xf32>

// CHECK-DAG: %[[b:.*]] = arith.constant dense<0.000000e+00> : tensor<1x32x42x128xf32>

  // CHECK: %0 = "tfl.conv_2d"(%arg0, %arg1, %cst)
}

// CHECK-LABEL: fuseAddIntoTransposeConv
func.func @fuseAddIntoTransposeConv(%arg0: tensor<1x32x42x128xf32>) -> tensor<1x64x84x32xf32> {
  %cst = arith.constant dense<1.5> : tensor<32xf32>
  %cst_0 = arith.constant dense<[1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0]> : tensor<16xf32>