module{
  func.func @main(%arg0: tensor<10xf32>) -> tensor<10xf32> {
    return %arg0 : tensor<10xf32>
  }
  func.func @func1(%arg0: tensor<10xf32>, %arg1: tensor<10xf32>) -> tensor<10xf32> {
    %0 = stablehlo.add %arg0, %arg1 : tensor<10xf32>
    %1 = stablehlo.add %arg0, %arg1 : tensor<10xf32>
    return %0 : tensor<10xf32>
  }
})mlir";

                             %arg2: tensor<5xf32> {tf._user_specified_name = "b1"},
                             %arg3: tensor<3920x10xf32> {tf._user_specified_name = "w2"},
                             %arg4: tensor<10xf32> {tf._user_specified_name = "b2"}) -> tensor<10xf32>

>, tensor<i8>, tensor<f64>, tensor<!tf_type.resource>)

# CHECK-SUBTYPE: func @main(%arg0: tensor<10xi32>, %arg1: tensor<!tf_type.variant<tensor<10xf32>>>, %arg2: tensor<10xi8>, %arg3: tensor<10xf64>, %arg4: tensor<!tf_type.resource<tensor<10xi32>>>) -> (tensor<10xi32>, tensor<!tf_type.variant<tensor<10xf32>>>, tensor<10xi8>, tensor<10xf64>, tensor<!tf_type.resource<tensor<10xi32>>>)...

    } : (tensor<*xf32>, tensor<144x10xf32>, tensor<10xf32>) -> tensor<*xf32>
    %5 = "quantfork.qcast"(%4) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.1>>
    %6 = "quantfork.dcast"(%5) : (tensor<*x!quant.uniform<i8:f32, 0.1>>) -> tensor<*xf32>
    func.return %6 : tensor<*xf32>
  }

  func.func private @composite_matmul_with_bias_fn_1(%a: tensor<*xf32>, %b: tensor<*xf32>, %c: tensor<*xf32>) -> tensor<*xf32> {

    %2 = "tf.Reshape"(%1, %cst_0) : (tensor<1x10xf32>, tensor<2xi32>) -> tensor<?x10xf32> loc(fused["Reshape:", "model/reshape"])
    %3 = "tf.AddV2"(%2, %cst) : (tensor<?x10xf32>, tensor<10xf32>) -> tensor<?x10xf32> loc(fused["AddV2:", "model/add"])
    func.return %3 : tensor<?x10xf32>
  }
}

// CHECK-LABEL: func @matmul_with_reshape_disable_quantization
// CHECK: "tf.PartitionedCall"

  %m0 = mhlo.constant dense<0> : tensor<i32>
  %t0 = "tf.Const"() { value = dense<0> : tensor<1xi32> } : () -> tensor<1xi32>
  %m1 = mhlo.constant dense<1> : tensor<i32>
  %t1 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m2 = mhlo.constant dense<1> : tensor<i32>
  %t2 = "tf.Const"() { value = dense<1> : tensor<1xi32> } : () -> tensor<1xi32>
  %m3 = mhlo.constant dense<1> : tensor<i32>

    %4 = "quantfork.stats"(%2) {layerStats = dense<[-2795.36523, 4609.57373]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>
    %5 = "tf.BiasAdd"(%4, %3) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
    %6 = "quantfork.stats"(%5) {layerStats = dense<[-2788.31055, 4616.62842]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>

// -----

func.func @testAddReluPack(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) {
   // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
   // CHECK: tac.device = "GPU", tac.inference_type = "FLOAT"
  %1 = "tfl.add"(%arg0, %0) {fused_activation_function = "RELU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

    %0 = "tf.ReadVariableOp"(%arg0) {device = ""} : (tensor<!tf_type.resource<tensor<*xf32>>>) -> tensor<*xf32>
    %1 = "tf.ReadVariableOp"(%arg1) {device = ""} : (tensor<!tf_type.resource<tensor<*xf32>>>) -> tensor<*xf32>
    %2 = "tf.Add"(%0, %1) {device = ""} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %2 : tensor<*xf32>
  }
  // CHECK: "tf_saved_model.global_tensor"()
  // CHECK:    sym_name = "dense/kernel"

    %4:2 = tf_executor.island wraps "tf.Add"(%arg0, %3) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %4#0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK: name: "foo"
// CHECK-NEXT: op: "_Arg"
// CHECK: name: "bar"