func.func @map_outside_compilation_0_inputs(%arg0 : tensor<2xi64>) -> () {
    // CHECK:             "tf._XlaHostComputeMlir"()
    "tf_device.cluster"() ({
      %1 = "tf.OpA"(%arg0) {_xla_map_outside_compilation = "0", _xla_outside_compilation = "from_launch"} : (tensor<2xi64>) -> tensor<2xi64>
      "tf.OpB"(%1) : (tensor<2xi64>) -> ()
      tf_device.return

  func.return %0 : tensor<4xi32>
}

func.func @i64() -> tensor<4xi64> {
  // CHECK-LABEL: @i64
  // CHECK: value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64> } : () -> tensor<4xi64>
  func.return %0 : tensor<4xi64>
}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string

    %cst = arith.constant dense<[2, 100]> : tensor<2xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>
    func.return %1 : tensor<2x100xf32>
  }

    %0 = "tf.XlaCallModule"(%output, %cst) <{Sout = [#tf_type.shape<10x1x3>], dim_args_spec = [], disabled_checks = [], function_list = [], has_token_input_output = false, module = "", platforms = ["CPU"], version = 9 : i64}> {_entry_function...

  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  %2 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

# CHECK: operandSegmentSizes = array<i32: 1, 1, 0, 1, 1>
# CHECK: resultSegmentSizes = array<i32: 0, 0, 0, 1>
# CHECK: tf_executor.fetch %[[output]] : tensor<*xi64>

node {
  name: "serilaized"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "value"
    value {
      tensor {

  %5 = "tf.Identity"(%4) {_replication_info = "cluster", _xla_compile_device_type = "TPU"} : (tensor<?xi64>) -> tensor<?xi64>

  func.return %2, %5 : tensor<?xi32>, tensor<?xi64>
}

// -----

// CHECK-LABEL: func @two_clusters
func.func @two_clusters(%arg0: tensor<*xi32>, %arg1: tensor<?xi64>) -> (tensor<?xi32>, tensor<?xi64>) {

      // CHECK:      name: "ParseExample"
      // CHECK-NEXT: op: "ParseExampleV2"
      // CHECK-NEXT: input: "input0"

  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ShapeOpI64
func.func @ShapeOpI64(%arg0 : tensor<576x72xf32>) -> tensor<2xi64> {
  %0 = "tfl.shape"(%arg0) : (tensor<576x72xf32>) -> tensor<2xi64>
  func.return %0 : tensor<2xi64>
  // CHECK: %[[CST:.*]] = arith.constant dense<[576, 72]> : tensor<2xi64>
  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ConstFoldStridedSlice

// MIN-MAX-CHECK-NEXT:  [[lhs:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%arg0) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_arg_0_calibration_method_1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32}> : (tensor<*xf32>) -> (tensor<*xf32>, tensor<f32>, tensor<f32>, tensor<0xi64>)