func.return %1 : tensor<1x28x23x2xf32>
  // CHECK: %[[CST:.*]] = "tf.Const{{.*}} dense<
  // CHECK-SAME: [1.000000e+00, 4.000000e+00], [3.000000e+00, 8.000000e+00], [5.000000e+00, 1.200000e+01]
  // CHECK-SAME: [7.000000e+00, 1.600000e+01], [9.000000e+00, 2.000000e+01], [1.100000e+01, 2.400000e+01]
  // CHECK-SAME: [1.300000e+01, 2.800000e+01], [1.500000e+01, 3.200000e+01], [1.700000e+01, 3.600000e+01]

// CHECK:           return %[[VAL_4]] : tensor<2x1x!quant.uniform<i8:f32, 3.000000e-03:-128>>
// CHECK:         }

// and function name containing conv.

func.func @qdq_for_conv_weight_empty(%arg0: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32> attributes {tf._original_func_name = "main_0"} {
  %cst = "tf.Const"() {value = dense<3.000000e-01> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
  %0 = "tf.XlaCallModule"(%arg0, %cst) {
    Sout = [#tf_type.shape<1x2x2x2>], _entry_function = @composite_conv_fn,
    _original_entry_function = "composite_conv_fn",

  // CHECK-SAME:    <{axis = 1 : i64,
  // CHECK-SAME:      axisStats = dense<{{\[}}[-0.000000e+00, 0.000000e+00],
  // CHECK-SAME:      [-1.000000e+00, 1.000000e+00],
  // CHECK-SAME:      [-2.000000e+00, 2.000000e+00]
  // CHECK-NEXT:    return %[[stat1]] : tensor<40x40xf32>
  // CHECK-NEXT:  }

  // Find the tensors and inject the min and max to the input and output

      platforms = [], version = 4 : i64
    } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>, tensor<2xf32>) -> tensor<1x2x2x2xf32>
    %2 = "quantfork.stats"(%1) {layerStats = dense<[0.000000e+00, 6.000000e+00]> : tensor<2xf32>} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
    return %2 : tensor<1x2x2x2xf32>
  }

  // CHECK-LABEL: composite_conv2d_with_bias_and_relu6_fn_10

	want(s, w)
}

func main() {

	// constants
	s := fmt.Sprintf("%f", -C1)
	want(s, "(-5.000000-6.000000i)")
	doprint(C1, "(5.000000+6.000000i)")

	// variables
	c1 := C1
	s = fmt.Sprintf("%f", c1)
	want(s, "(5.000000+6.000000i)")
	doprint(c1, "(5.000000+6.000000i)")

	// 128
	c2 := complex128(C1)
	s = fmt.Sprintf("%G", c2)
	want(s, "(5+6i)")

	// real, imag, complex

    func.func @fully_quantized_add(%arg0: tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>>) -> tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>> {
      %0 = stablehlo.add %arg0, %arg0 : tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>>
      return %0 : tensor<2x!quant.uniform<i8:f32, 1.000000e+00:0>>
    }
  )mlir";

  OwningOpRef<ModuleOp> module_op = ParseModuleOpString(kFullyQuantizedAdd);

  %0 = "tf.Const"() {value = dense<[[[[0.000000e+00, 0.000000e+00], [0.000000e+00, 1.000000e+00], [0.000000e+00, 2.000000e+00], [0.000000e+00, 3.000000e+00]], [[1.000000e+00, 0.000000e+00], [1.000000e+00, 1.000000e+00], [1.000000e+00, 2.000000e+00], [1.000000e+00, 3.000000e+00]], [[2.000000e+00, 0.000000e+00], [2.000000e+00, 1.000000e+00], [2.000000e+00, 2.000000e+00], [2.000000e+00, 3.000000e+00]], [[3.000000e+00, 0.000000e+00], [3.000000e+00, 1.000000e+00], [3.000000e+00, 2.000000e+00],...

  %0 = "vhlo.constant_v1"() <{value = #vhlo.tensor_v1<dense<0.000000e+00> : tensor<f32>>}> : () -> tensor<1x1x1x96xf32>
  func.return %0 : tensor<1x1x1x96xf32>
 }
}

//CHECK: func.func @main() -> tensor<1x1x1x96xf32> attributes {tf.entry_function = {outputs = "vhlo.constant_v1"}} {
//CHECK-NEXT:  %0 = "vhlo.constant_v1"() <{value = #vhlo.tensor_v1<dense<0.000000e+00> : tensor<1x1x1x96xf32>>}> : () -> tensor<1x1x1x96xf32>

// Ensure bucketize roundtrip exactly

func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> {
  // CHECK-LABEL: @main
  // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32>
  %0 = "tfl.bucketize"(%arg0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32>
  func.return %0 : tensor<3x2xi32>