func.func @main(%arg0: tensor<f32>) {
      %elem = "tf.Const"() <{value = dense<0.000000e+00> : tensor<f32>}> : () -> tensor<f32>
      %shape = "tf.Const"() <{value = dense<> : tensor<0xi64>}> : () -> tensor<0xi64>
      %zero = "tf.Fill"(%shape, %elem) : (tensor<0xi64>, tensor<f32>) -> tensor<f32>
      tf_device.replicate([%arg0, %zero] as %arg1: tensor<f32>) {n = 2 : i32} {
        %arg1_id = "tf_device.launch"() <{device = "TPU_REPLICATED_HOST_0"}> ({

using ::xla::XlaComputation;
using ::xla::XlaOp;

static constexpr char kMlirModuleStr[] = R"(
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1442 : i32}} {

func.func @main(%arg0: tensor<10x8x9xf32>) -> tensor<2xi64> {
  %elem_shape = "tf.Const"() {value = dense<[8, 9]> : tensor<2xi32>} : () -> tensor<2xi32>
  %tl = "tf.TensorListFromTensor"(%arg0, %elem_shape) : (tensor<10x8x9xf32>, tensor<2xi32>) -> tensor<!tf_type.variant<tensor<8x9xf32>>>
  // CHECK: %[[SHAPE:.*]] = "tf.Const"() <{value = dense<[8, 9]> : tensor<2xi64>}> : () -> tensor<2xi64>

  %2 = "tf.LookupTableFindV2"(%0, %arg, %default) {device = "/CPU:0"} : (tensor<!tf_type.resource>, tensor<?x!tf_type.string>, tensor<i64>) -> tensor<*xi64>
  func.return %1, %2 : tensor<i64>, tensor<*xi64>
}

}

// -----

module attributes {tf_saved_model.semantics} {

// Test hoisting const op.

// CHECK-LABEL: func @_tfrt_resource_init

  %cst = arith.constant dense<[2, 2]> : tensor<2xi32>
  %list = "tf.TensorListFromTensor"(%arg0, %cst) : (tensor<3x2x2xf32>, tensor<2xi32>) -> tensor<!tf_type.variant<tensor<f32>>>
  %t:2 = "tf.TensorListConcatV2"(%list, %cst, %lead) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<2xi32>, tensor<i64>) -> (tensor<?x2xf32>, tensor<0xi64>)
  func.return %t#0, %t#1 : tensor<?x2xf32>, tensor<0xi64>

    %cst = "tf.Const"() { value = dense<[1, 1, 300]> : tensor<3xi64> } : () -> tensor<3xi64>
    %0 = "tf.XlaGather"(%cst_0, %arg0, %cst) {dimension_numbers = "\0A\02\00\01\12\01\00\1A\02\00\01\20\01", indices_are_sorted = true} : (tensor<200x100x300xf32>, tensor<10x2xi32>, tensor<3xi64>) -> tensor<1x300x10xf32>
    %1 = "tf.Identity"(%0) {device = ""} : (tensor<1x300x10xf32>) -> tensor<1x300x10xf32>

  }
  func.return %0 : tensor<i32>
}

// CHECK-LABEL: func @generator_op
func.func @generator_op(%str : tensor<!tf_type.string>, %arg0: tensor<*x!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<*xi64>, %arg3: tensor<!tf_type.string>) {
  tf_executor.graph {
    tf_executor.island {
      // CHECK: %{{.*}}, %[[CONTROL:[^ ,]*]] = tf_executor.island wraps "tf.GeneratorDataset"

    %strides = "tf.Const"() {value = dense<[3, 1, 1]> : tensor<3xi64>} : () -> tensor<3xi64>
    // CHECK: %0 = "tf.XlaConvV2"(%arg0, %arg1, %cst_3, %cst_2, %cst_1, %cst_0, %cst) <{dimension_numbers = "\18\03 \042\03\00\01\02@\04P\04Z\03\01\02\03b\03\01\02\03", precision_config = ""}> : (tensor<8x4x16x16x16xf32>, tensor<4x3x3x16x16xf32>, tensor<3xi64>, tensor<3x2xi32>, tensor<3xi32>, tensor<3xi32>, tensor<i32>) -> tensor<8x4x14x14x16xf32>

//     -> tensor<5x2xf32>
//
// is lowered to
//
//   %shape = "tf.Const"() {value = dense<[-1, 2]> : tensor<2xi64>}
//   %inp0 = "tf.Reshape"(%arg0, %shape)
//     : (tensor<2xf32>, tensor<2xi64>) -> tensor<1x2xf32>
//   %inp1 = "tf.Reshape"(%arg1, %shape)
//     : (tensor<2x2x2xf32>, tensor<2xi64>) -> tensor<4x2xf32>
//   %items0 = "tf.Unpack"(%[[INP0]]) {axis = 0 : i64}
//     : (tensor<1x2xf32>) -> tensor<2xf32>

  // CHECK-SAME: -> tensor<15xf32>
  // CHECK: %[[LENS:.*]] = "tf.Const"() <{value = dense<3> : tensor<5xi64>}> : () -> tensor<5xi64>
  %concat:2 = "tf.TensorArrayConcatV3"(%ta#0, %ta#1) {element_shape_except0 = #tf_type.shape<*>} : (tensor<!tf_type.resource>, tensor<f32>) -> (tensor<*xf32>, tensor<*xi64>)
  // CHECK: %[[SPLIT_RESHAPE:.*]] = "tf.Reshape"(%[[CONCAT_RESHAPE]],
  // CHECK-SAME: -> tensor<5x3xf32>