func.func @device_test(%arg0: tensor<3x1xf32>) -> (tensor<3x3xf32>) {

  // CHECK: device = "gpu"
  %0 = "tf.Const"() {value = dense<[[1.0, 2.0, 3.0]]> : tensor<1x3xf32>} : () -> tensor<1x3xf32>
  // 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"

  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "xpu"
  %3 = "tf.Relu"(%2) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"]} : (tensor<3x3xf32>) -> tensor<3x3xf32>
  func.return %3 : tensor<3x3xf32>
}

  // CHECK-NEXT: [[r1:%.*]] = tfrt_fallback_async.executeop {{.*}} "tf.BiasAdd"([[r0]], [[result]])
  %3 = "tf.BiasAdd"(%2, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], data_format = "NHWC", device = "/device:CPU:0"} : (tensor<3x3xf32>, tensor<3xf32>) -> tensor<3x3xf32>

# CHECK-NEXT: [[R0:%.*]] = "tf.ReadVariableOp"([[ARG1]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: [[R1:%.*]] = "tf.MatMul"([[ARG0]], [[R0]]) <{{{.*}}}> {device = ""} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return [[R1]] : tensor<3x3xf32>


def Test():

  x = tf.constant([[1.0], [1.0], [1.0]])
  y = tf.compat.v1.get_variable(
      name='y',

  %1 = "tf.MatMul"(%arg0, %0) {T = f32, device = "/device:CPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  func.return %1 : tensor<3x3xf32>
}

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

  func.return %2 : tensor<3x3xf32>

# CHECK-NEXT: %[[READ_VAR_0:.*]] = "tf.ReadVariableOp"(%[[ARG_2]]) {{{.*}}} : (tensor<!tf_type.resource<tensor<1x3xf32>>>) -> tensor<1x3xf32>
# CHECK-NEXT: %[[MATMUL_0:.*]] = "tf.MatMul"(%[[ARG_1]], %[[READ_VAR_0]]) <{{{.*}}}> {{{.*}}} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
# CHECK-NEXT: return %[[MATMUL_0]] : tensor<3x3xf32>


def Test():
  x = tf.constant([[1.0], [1.0], [1.0]])
  y = tf.compat.v1.get_variable(
      name='y',

module {
func.func @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
  %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<i64>) -> tensor<6x3xf32>
  func.return %1 : tensor<6x3xf32>
}

// CHECK-NEXT:    %6 = "tf.Select"(%4, %3, %arg4) : (tensor<i1>, tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
// CHECK-NEXT:   %7 = "tf.Identity"(%4) : (tensor<i1>) -> tensor<i1>
// CHECK-NEXT:    %8 = "tf.MatMul"(%5, %arg7) : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>
// CHECK-NEXT:    %9 = "tf.Select"(%7, %8, %arg7) : (tensor<i1>, tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<3x3xf32>

module {
func.func @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
  %0 = "tfl.custom"(%arg0, %arg1) {custom_code = "stablehlo.concatenate", custom_option = #tfl<const_bytes : "0x64696D656E73696F6E00010B0101010004022401">} : (tensor<3x3xf32>, tensor<3x3xf32>) -> tensor<6x3xf32>
  func.return %0 : tensor<6x3xf32>
}
}

// CHECK:       module {