func.return %2 : tensor<1x32x32x16x!quant.uniform<i8:f32, 1.0>>
}

// -----

// CHECK-LABEL: @test_conv2d_replace_float
func.func @test_conv2d_replace_float(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x16xf32> {

// -----

func.func @ensureBiasForConv2d(%arg0: tensor<128x32x32x3xf32>, %arg1: tensor<32x1x1x3xf32>) -> tensor<128x32x32x32xf32> {
  %cst = "tfl.no_value"() {value = unit} : () -> none
  %0 = "tfl.conv_2d"(%arg0, %arg1, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<128x32x32x3xf32>, tensor<32x1x1x3xf32>, none) -> tensor<128x32x32x32xf32>

    "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>)

// CHECK:  %1 = "tfl.transpose"(%arg0, %cst) : (tensor<4x8x32x32xf32>, tensor<4xi32>) -> tensor<4x32x32x8xf32>
// CHECK:  %cst_0 = arith.constant dense<64> : tensor<2xi32>
// 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>

    ```

    will be replaced by `_xla_compile_device_type`, with its value set to the value of `device`.

    ```mlir

  %0 = "tf.Conv2D"(%arg0, %arg1) {data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<?x32x32x6xf32>, tensor<3x3x3x16xf32>) -> tensor<?x8x7x16xf32>
  func.return %0 : tensor<?x8x7x16xf32>
}