%3 = "tf.Squeeze"(%2) {squeeze_dims = [3]} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>
  %4 = "tf.Pad"(%3, %cst_3) : (tensor<4x64x64xf32>, tensor<3x2xi32>) -> tensor<4x64x64xf32>
  %5 = "tf.BatchToSpaceND"(%4, %cst, %cst_2) : (tensor<4x64x64xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<1x128x128xf32>

module {
func.func @main(%arg0: tensor<4x68x68x3xf32>, %arg1: tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32> {
  %0 = "tf.Conv2D"(%arg0, %arg1) {padding = "VALID", strides = [1, 1, 1, 1]} : (tensor<4x68x68x3xf32>, tensor<5x5x3x8xf32>) -> tensor<4x64x64x8xf32>
  func.return %0 : tensor<4x64x64x8xf32>
}

// CHECK:  %2 = "tfl.resize_nearest_neighbor"(%1, %cst_0) <{align_corners = false, half_pixel_centers = true}> : (tensor<4x32x32x8xf32>, tensor<2xi32>) -> tensor<4x64x64x8xf32>
// CHECK:  %cst_1 = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>
// CHECK:  %3 = "tfl.transpose"(%2, %cst_1) : (tensor<4x64x64x8xf32>, tensor<4xi32>) -> tensor<4x8x64x64xf32>

func.func @PadStridedSliceNewAxisMask2(%arg0: tensor<4x64x64x1xf32>) -> tensor<1x4x64x64xf32> {
  %cst = arith.constant dense<0> : tensor<3xi32>
  %cst_0 = arith.constant dense<1> : tensor<3xi32>
  %0 = "tf.Squeeze"(%arg0) {T = f32, _output_shapes = ["tfshape$dim { size: 4 } dim { size: 64 } dim { size: 64 }"], device = "", squeeze_dims = []} : (tensor<4x64x64x1xf32>) -> tensor<4x64x64xf32>