%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"

    return %arg2 : tensor<?xi32>
  }

  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-SAME:   dim_args_spec = []

  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v1", type = tensor<?xf32>, value = dense<1.> : tensor<1xf32> } : () -> ()
  // expected-error@+1 {{Incompatible code paths}}

    // CHECK:     return %[[RESULT]]
    func.return %0 : tensor<10xi32>
  }

  // CHECK-LABEL: func @foo
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @foo(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {

// results, and function body should be the same.

// CHECK-LABEL: module
module {
  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-NOT:    function_list

  ^bb0(%arg2: tensor<*xi32>, %arg3: tensor<*xf32>):
    %1:2 = func.call @WhileOp_body(%arg2, %arg3) : (tensor<*xi32>, tensor<*xf32>) -> (tensor<*xi32>, tensor<*xf32>)
    "tfl.yield"(%1#0, %1#1) : (tensor<*xi32>, tensor<*xf32>) -> ()
  }) : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>) loc("WhileOp")
  func.return %0#1 : tensor<1xf32>

    %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>

func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "Add"}} {
  %graph = tf_executor.graph {
    %2:2 = tf_executor.island wraps "tf.Add"(%arg0, %arg1) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %2 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

  %5 = "stablehlo.concatenate"(%4, %1) {dimension = 0 : i64} : (tensor<?xf32>,  tensor<?xf32>) -> tensor<?xf32>
  %6 = "quantfork.stats"(%5) {layerStats = dense<[-1.5726943, 4.6875381]> : tensor<2xf32>} : (tensor<?xf32>) -> tensor<?xf32>
  func.return  %3, %6 : tensor<?xf32>, tensor<?xf32>
}

// -----