// CHECK: %[[VAL_1:.*]] = "tf.Identity"(%[[VAL_0]]) : (tensor<*xf32>) -> tensor<*xf32>
  // CHECK: return %[[VAL_1]]
  %0 = "tf.Conv2D"(%arg2, %arg1) {data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>) -> tensor<*xf32>

  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>

      tf_executor.fetch %outputs_2 : tensor<*xi1>
    }
    return %0 : tensor<*xi1>
  }
  func.func private @image_sample_write_summary_summary_cond_false_710(%arg0: tensor<3x32x32x3xf32>, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<i64>) -> tensor<*xi1> attributes {tf._construction_context = "kEagerRuntime"} {
    %0 = tf_executor.graph {

// 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>