%w = arith.constant dense<2.0> : tensor<3x3x3x3xf32>
  %q = "tfl.quantize"(%w) {qtype = tensor<3x3x3x3x!quant.uniform<i8<-127:127>:f32:0,{1.0,2.0,3.0}>>} : (tensor<3x3x3x3xf32>) -> tensor<3x3x3x3x!quant.uniform<i8<-127:127>:f32:0,{1.0,2.0,3.0}>>
  %dq = "tfl.dequantize"(%q) : (tensor<3x3x3x3x!quant.uniform<i8<-127:127>:f32:0,{1.0,2.0,3.0}>>) -> tensor<3x3x3x3xf32>

// CHECK:           %[[VAL_2:.*]] = "tf.Conv2D"(%[[VAL_0]], %[[VAL_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [0, 0, 1, 1, 1, 1, 0, 0], padding = "EXPLICIT", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<64x8x8x8xf32>, tensor<8x8x8x64xf32>) -> tensor<64x3x3x64xf32>
// CHECK:           return %[[VAL_2]] : tensor<64x3x3x64xf32>

// -----

// CHECK-LABEL: func @diag_part
// CHECK-SAME: %[[ARG:.*]]: tensor<4x3x4x3xf32>
func.func @diag_part(%arg0: tensor<4x3x4x3xf32>) -> tensor<4x3xf32> {
  // CHECK: %[[RS:.*]] = mhlo.reshape %[[ARG]] : (tensor<4x3x4x3xf32>) -> tensor<12x12xf32>
  // CHECK-DAG: %[[IOTA0:.*]] = "mhlo.iota"() <{iota_dimension = 0 : i64}> : () -> tensor<12x12xi32>