%cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, 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>

  %1 = "arith.constant"() {value = dense<0> : tensor<16xi64>} : () -> tensor<16xi64>
  %2 = "tfl.conv_2d"(%arg0, %0, %1) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} : (tensor<1x32x32x8x!quant.uniform<i16:f32, 1.0>>, tensor<16x1x1x8x!quant.uniform<i8:f32, 1.0>>, tensor<16xi64>) -> tensor<1x32x32x16x!quant.uniform<i16:f32, 1.0>>

    %outputs_11, %control_12 = tf_executor.island wraps "tf.Const"() {value = dense<[0, 1, 2]> : tensor<3xi64>} : () -> tensor<3xi64>

  // CHECK-LABEL: init_all_tables
  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

  %1 = "tfr.constant_tensor"(%0) : (!tfr.attr) -> !tfr.tensor
  func.return %1 : !tfr.tensor

// CHECK-NEXT: %[[RES:.*]] = "tf.Const"() <{value = dense<[1, -1, 3]> : tensor<3xi64>}> : () -> tensor<3xi64>
// CHECK-NEXT: "tfr.cast"(%[[RES]]) : (tensor<3xi64>) -> !tfr.tensor
// CHECK-NEXT: return
}

// -----

// CHECK-LABEL: constant_tensor_scalar
func.func @constant_tensor_scalar() -> !tfr.tensor {

using func::FuncOp;
using mlir::ModuleOp;

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

func.func @lift_float_gather(%arg0: tensor<6xi64>) -> (tensor<6x32xf32>) {
  %cst = "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %cst_0 = "tf.Const"() {device = "", value = dense<1.0> : tensor<128x32xf32>} : () -> tensor<128x32xf32>
  %0 = "tf.GatherV2"(%cst_0, %arg0, %cst) {batch_dims = 0 : i64, device = ""} : (tensor<128x32xf32>, tensor<6xi64>, tensor<i32>) -> tensor<6x32xf32>
  return %0 : tensor<6x32xf32>

  %2 = "tf.Shape"(%0) {device = "/CPU:0"} : (tensor<*xi32>) -> tensor<?xi64>
  %dyn_2 = "tf.SetStaticDimensionBounds" (%2, %arg4) :(tensor<?xi64>, tensor<?xi64>) -> tensor<?xi64>
  %compilation_status, %program = "tf._TPUCompileMlir"(%1, %2) {device = "/CPU:0", metadata = "metadata", mlir_module = "mlir_module"} : (tensor<?xi64>, tensor<?xi64>) -> (tensor<!tf_type.string>, tensor<3x!tf_type.string>)

# CHECK: resultSegmentSizes = array<i32: 2, 2, 2, 2, 0, 0>
# CHECK: tf_executor.fetch %[[parse_example]]#0, %[[parse_example]]#7 : tensor<*xi64>, tensor<*xf32>

node {
  name: "input0"
  op: "Placeholder"
  attr {
    key: "dtype"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "shape"
    value {
      shape {

  %values = "tf.Const"() {value = dense<[1, 2, 3, 4]> : tensor<4xi64>} : () -> tensor<4xi64>
  "tf.LookupTableImportV2"(%handle, %keys, %values) {device = ""} : (tensor<!tf_type.resource>, tensor<4x!tf_type.string>, tensor<4xi64>) -> ()