```mlir
      func @test(%arg0: tensor<*xi32>) {
        "tf_device.cluster_func"(%arg0) {
            func = @func,
            step_marker_location = ""} : (tensor<*xi32>) -> tensor<*xi32>
        return
      }

      func @func(%arg0: tensor<*xi32>) -> tensor<*xi32> {
        %0 = "tf.XlaSharding"(%arg0) {_XlaSharding = "\01\02\03",

// RUN: odml-to-stablehlo-opt %s -tfl-parse-stablehlo-ops | FileCheck -dump-input always %s

module {
  func.func @main(%arg0: tensor<8x8x1x207xf32>, %arg1: tensor<3x3x16x207xf32>) -> tensor<16x8x8x1xf32> {

func.func @main(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32> {
^bb0(%arg0: tensor<40x37xf32>, %arg1: tensor<40x37xf32>):
  %cst = arith.constant dense<1.0> : tensor<40xf32>

func.func @reshape_float(%arg0: tensor<2x3x4xf32>) -> tensor<6x4xf32> {
  %0 = stablehlo.reshape %arg0 : (tensor<2x3x4xf32>) -> tensor<6x4xf32>
  return %0 : tensor<6x4xf32>
}
// CHECK-LABEL: reshape_float
// CHECK-NOT: tfl.reshape
// CHECK: stablehlo.reshape

// -----

// Tests that a quantized `stablehlo.select` is converted to `tfl.select_v2`.

func.func @select(
    %arg0: tensor<1x3xi1>,

// CHECK-LABEL: @test_conv2d_float
func.func @test_conv2d_float(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x16xf32> {
  // CHECK-DAG: %[[VAL0:.+]] = "tfl.pseudo_const"() <{value = dense<42> : tensor<16x1x1x8xi8>}>
  // CHECK-DAG: %[[VAL1:.+]] = "tfl.pseudo_const"() <{value = dense<1> : tensor<16x1x1x8xi8>}>

      tf_executor.fetch %c2 : !tf_executor.control
    }
    return %arg0, %arg1 : tensor<i32>, tensor<i32>
  }

  // CHECK-LABEL: func @handles_variables_in_while_loops
  func.func @handles_variables_in_while_loops(%arg0: tensor<!tf_type.resource<tensor<i32>>> {tf._user_specified_name = "arg0", tf.device = "/job:localhost/replica:0/task:0/device:CPU:0", tf_saved_model.index_path = []})

}

// -----

// CHECK-LABEL: func @testTFDialect
func.func @testTFDialect(%arg0: tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string> {
  %0 = "tf.Identity"(%arg0) : (tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string>
  func.return %0 : tensor<4x2x!tf_type.string>
}


// -----

func.func @testTFDialect(%arg0: tensor<4x?x!tf_type.stringref>) -> tensor<4x2x!tf_type.string> {

}

// -----

// Test lowering of tf call ops

// CHECK-LABEL: @callee
func.func @callee(%arg0: tensor<i32>) -> (tensor<i32>) {
  func.return %arg0: tensor<i32>
}

// CHECK-LABEL: func @call_test
func.func @call_test(%arg0: tensor<i32>) -> (tensor<i32>, tensor<i32>, tensor<i32>) {
  %0 = "tf.Add"(%arg0, %arg0) {__op_key = 0, device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>

func.func @main(%arg0 : tensor<!tf_type.variant<tensor<2xi32>>>, %arg1: tensor<!tf_type.variant<tensor<*xi32>>>) -> tensor<!tf_type.variant<tensor<2xi32>>> {
  func.return %arg0 : tensor<!tf_type.variant<tensor<2xi32>>>
}

  // CHECK: return %[[ARG0]], %[[ARG0]]
}

// CHECK-LABEL: @min_with_f32_max_val
// CHECK-SAME: (%[[ARG0:.+]]: tensor<f32>)
func.func @min_with_f32_max_val(%arg0 : tensor<f32>) -> (tensor<f32>, tensor<f32>) {
  %f32_max = arith.constant dense<3.40282347E+38> : tensor<f32>
  %0 = "tfl.minimum"(%arg0, %f32_max) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %1 = "tfl.minimum"(%f32_max, %arg0) : (tensor<f32>, tensor<f32>) -> tensor<f32>