if (!quantDenseAttr) {
    return nullptr;
  }

  // Cast from an expressed-type-based type to storage-type-based type,
  // preserving the sparse shape (i.e. tensor<4xf32> -> tensor<4xi8>).
  ShapedType newSparseType = mlir::dyn_cast_or_null<ShapedType>(
      quantizedElementType.castExpressedToStorageType(
          realSparseAttr.getType()));
  if (!newSparseType) {
    return nullptr;
  }

  // CHECK: %[[CONVERT_1:.*]] = mhlo.bitcast_convert %[[QUANTIZE]] : (tensor<4x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<4xi8>
  // CHECK: %[[CONVERT_2:.*]] = mhlo.bitcast_convert %[[CONVERT_1]] : (tensor<4xi8>) -> tensor<4x!quant.uniform<i8:f32, 1.000000e+00:3>>

func.func @testMaximumOfZeroToReluInt64OnGpu(%arg0: tensor<4xi64>) -> tensor<4xi64> {
  // CHECK: %[[RESULT:.*]] = "tf.Relu"(%arg0) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<4xi64>) -> tensor<4xi64>
  // CHECK: return %[[RESULT]]
  %cst_0 = arith.constant dense<0> : tensor<i64>
  %0 = "tf.Maximum"(%arg0, %cst_0) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<4xi64>, tensor<i64>) -> tensor<4xi64>

func.func @any(%input: tensor<4x8xi1>) -> tensor<4xi1> {
  %dims = "tf.Const"() { value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  // CHECK: %[[INIT:.*]] = mhlo.constant dense<false> : tensor<i1>
  // CHECK: mhlo.reduce(%{{.*}} init: %[[INIT]]) applies mhlo.or across dimensions = [1] : (tensor<4x8xi1>, tensor<i1>) -> tensor<4xi1>
  %0 = "tf.Any"(%input, %dims) : (tensor<4x8xi1>, tensor<1xi32>) -> tensor<4xi1>
  func.return %0 : tensor<4xi1>
}

aWRnaXRzIFB0eSBMdGQwHhcNMTIwOTEyMjE1MjAyWhcNMTUwOTEyMjE1MjAyWjBF
MQswCQYDVQQGEwJBVTETMBEGA1UECAwKU29tZS1TdGF0ZTEhMB8GA1UECgwYSW50
ZXJuZXQgV2lkZ2l0cyBQdHkgTHRkMFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBANLJ
hPHhITqQbPklG3ibCVxwGMRfp/v4XqhfdQHdcVfHap6NQ5Wok/4xIA+ui35/MmNa
rtNuC+BdZ1tMuVCPFZcCAwEAAaNQME4wHQYDVR0OBBYEFJvKs8RfJaXTH08W+SGv
zQyKn0H8MB8GA1UdIwQYMBaAFJvKs8RfJaXTH08W+SGvzQyKn0H8MAwGA1UdEwQF
MAMBAf8wDQYJKoZIhvcNAQEFBQADQQBJlffJHybjDGxRMqaRmDhX0+6v02TUKZsW

  %begin0 = arith.constant dense<0> : tensor<4xi64>
  %shape0 = arith.constant dense<[2,3,4,4]> : tensor<4xi64>
  %begin1 = arith.constant dense<1> : tensor<4xi64>
  %shape1 = arith.constant dense<[1,2,3,4]> : tensor<4xi64>
  %0 = "tfl.slice"(%arg0, %begin0, %shape0) : (tensor<2x3x4x5xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<2x3x4x4xf32>

// CHECK-LABEL: select_v2_multidim
// CHECK:  "tfl.select_v2"(%arg0, %arg1, %arg2)
// CHECK:  return
}

func.func @select_v2_broadcast(%arg0: tensor<4xi1>, %arg1: tensor<3x4xf32>, %arg2: tensor<8x3x4xf32>) -> tensor<8x3x4xf32> {
  %0 = "tf.SelectV2"(%arg0, %arg1, %arg2) : (tensor<4xi1>, tensor<3x4xf32>, tensor<8x3x4xf32>) -> tensor<8x3x4xf32>
  func.return %0: tensor<8x3x4xf32>

// CHECK-LABEL: select_v2_broadcast

aWRnaXRzIFB0eSBMdGQwHhcNMTIwOTEyMjE1MjAyWhcNMTUwOTEyMjE1MjAyWjBF
MQswCQYDVQQGEwJBVTETMBEGA1UECAwKU29tZS1TdGF0ZTEhMB8GA1UECgwYSW50
ZXJuZXQgV2lkZ2l0cyBQdHkgTHRkMFwwDQYJKoZIhvcNAQEBBQADSwAwSAJBANLJ
hPHhITqQbPklG3ibCVxwGMRfp/v4XqhfdQHdcVfHap6NQ5Wok/4xIA+ui35/MmNa
rtNuC+BdZ1tMuVCPFZcCAwEAAaNQME4wHQYDVR0OBBYEFJvKs8RfJaXTH08W+SGv
zQyKn0H8MB8GA1UdIwQYMBaAFJvKs8RfJaXTH08W+SGvzQyKn0H8MAwGA1UdEwQF
MAMBAf8wDQYJKoZIhvcNAQEFBQADQQBJlffJHybjDGxRMqaRmDhX0+6v02TUKZsW

// e.g.
// [y1, y2] = Square([x1, x2]) where x and y are complex.
// Writing
// x = [x1_real, x1_imag, x2_real, x2_imag]
// y = [y1_real, y1_imag, y2_real, y2_imag]
// the Jacobian transpose is
// the 4x4 matrix:
// dy1_real/dx1_real dy1_imag/dx1_real dy2_real/dx1_real dy2_imag/dx1_real
// dy1_real/dx1_imag dy1_imag/dx1_imag dy2_real/dx1_imag dy2_imag/dx1_imag
// dy1_real/dx2_real dy1_imag/dx2_real dy2_real/dx2_real dy2_imag/dx2_real

  %0 = "tfl.shape"(%arg0) : (tensor<8x7x6x5x?x3x2x1xf32>) -> tensor<8xi64>
  %1 = "tfl.shape"(%arg1) : (tensor<?x3x2x1xf32>) -> tensor<4xi64>
  %2 = "tfl.broadcast_args"(%0, %1) : (tensor<8xi64>, tensor<4xi64>) -> tensor<8xi64>
  %3 = "tfl.broadcast_to"(%arg0, %2) : (tensor<8x7x6x5x?x3x2x1xf32>, tensor<8xi64>) -> tensor<8x7x6x5x?x3x2x1xf32>