"tf.SomeOp2"() {device = "/device:CPU:0", _output_shapes = ["tfshape$"], f.Tin = [f32], f._read_only_resource_inputs = []} : () -> ()
  func.return
}

// CHECK-LABEL: func @basic
func.func @basic(
    %arg0: tensor<3x1xf32>,
    %arg1: tensor<!tf_type.resource<tensor<1x3xf32>>>) -> (tensor<3x3xf32>) {

  func.return
}

// CHECK-LABEL: func @some_args
// CHECK-SAME: (%arg0: tensor<i1>, %arg1: tensor<!tf_type.resource<tensor<f32>>> {tf.resource_name = "x"})
// CHECK-NOT: "tf.VarHandleOp"
func.func @some_args(%arg0: tensor<i1>) {

tensor<?x8xi1>) -> (tensor<8x10xf32>, tensor<?x8x10xf32>, tensor<8x10xf32>, tensor<8x10xf32>, tensor<f32>) attributes {tf._input_shapes = ["tfshape$dim { size: -1 } dim { size: 8 } dim { size: 8 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$dim { size: 8 } dim { size: 10 }", "tfshape$unknown_rank: true", "tfshape$unknown_rank: false", "tfshape$unknown_rank: false", "tfshape$dim { size: -1 } dim { size: 8 }"], tf.api_implements = "lstm_b4e9f0e7-ac55-42bc-8ef2-8496419a608c", tf.api_preferred_device =...

  %0 = "tf.ReadVariableOp"(%handle) {_output_shapes = ["tfshape$dim { size: 3 }"], device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<3xf32>>>) -> tensor<3xf32>
  %1 = "tf.ReadVariableOp"(%arg1) {_output_shapes = ["tfshape$dim { size: 1 } dim { size: 3 }"], device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>

  // CHECK: device = "xpu"
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "xpu"

  // CHECK: device = "gpu"
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "gpu"

      %outputs_3, %control_4 = tf_executor.island wraps "tf.Placeholder"() {device = "", dtype = "tfdtype$DT_FLOAT", name = "x", shape = "tfshape$dim { }"} : () -> tensor<0xf32>
      %outputs_5, %control_6 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_3) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input0"} : (tensor<0xf32>) -> tensor<0xf32>

      -> (tensor<3x3xf32> {tf_saved_model.index_path = []}) {
  // CHECK: {{%.*}} = corert.get_op_handler %arg0 "/device:GPU:0"
  %2 = "tf.MatMul"(%arg0, %arg1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:GPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  func.return %2 : tensor<3x3xf32>

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.VarHandleOp"() {container = "a", device = "/CPU:0", dtype = i64, shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<i64>>> loc("x")

  // CHECK: %[[RES:.*]] = "tf.Const"()
  // CHECK: return %[[RES]]
  %0 = "tf.Const"() {value = dense<4.200000e+01> : tensor<f32>} : () -> tensor<f32>
  %1 = "tf.VarHandleOp"() {container = "", shape = "tfshape$", shared_name = "x"} : () -> tensor<!tf_type.resource<tensor<f32>>>
  "tf.AssignVariableOp"(%1, %0) : (tensor<!tf_type.resource<tensor<f32>>>, tensor<f32>) -> ()
  func.return
}

// -----