%a:2, %control1 = tf_executor.island wraps "tf.A"() {_class = ["loc:@class"]} : () -> (tensor<2xf32>, tensor<2xf32>)
    %s:2, %control2 = tf_executor.island wraps "tf.Split"(%c, %a#1) {num_split = 2 : i32} : (tensor<i32>, tensor<2xf32>) -> (tensor<1xf32>, tensor<1xf32>)
    tf_executor.fetch
  }
  func.return
}

// -----

// CHECK-LABEL: func @no_colocate_split_has_device

    %4 = "tf.LayerNorm"(%a1, %a2, %a3, %a4) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
   "tfl.yield"(%4) : (tensor<128x128xf32>) -> ()
  }) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

    %r_max_without_zero = "tf.Max"(%input_1d, %dim) { keep_dims = true }: (tensor<?xf32>, tensor<1xi64>) -> tensor<1xf32>
    %r_max = "tf.Maximum"(%zero, %r_max_without_zero) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

    %r_min_without_zero = "tf.Min"(%input_1d, %dim) { keep_dims = true }: (tensor<?xf32>, tensor<1xi64>) -> tensor<1xf32>

  ^bb1(%0: tensor<!tf_type.resource<tensor<?xf32>>>, %1: tensor<!tf_type.resource<tensor<?xf32>>>, %2: tensor<!tf_type.resource<tensor<?xf32>>>):
    "tf.AssignVariableOp"(%0, %arg0) : (tensor<!tf_type.resource<tensor<?xf32>>>, tensor<?xf32>) -> ()
    cf.br ^bb1(%1, %2, %0 : tensor<!tf_type.resource<tensor<?xf32>>>, tensor<!tf_type.resource<tensor<?xf32>>>, tensor<!tf_type.resource<tensor<?xf32>>>)
  }

// CHECK-NEXT: }

func.func @main(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  %0 = "tfl.less"(%arg0, %arg1) : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xi1>
  %1 = "tf.If"(%0, %arg0, %arg1) {else_branch = @cond_false, then_branch = @cond_true, is_stateless = false} : (tensor<1xi1>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  func.return %1 : tensor<1xf32>
}

  %cst_0 = "tf.Const"() {value = dense<0.0235294122> : tensor<f32>} : () -> tensor<f32>
  %cst_1 = "tf.Const"() {value = dense<0.00708661414> : tensor<1xf32>} : () -> tensor<1xf32>
  %cst_2 = "tf.Const"() {value = dense<1.799000e+03> : tensor<1xf32>} : () -> tensor<1xf32>

func.func @func_0_CPU(%arg0: tensor<10x10x10xf32>, %arg1: tensor<10xf32>) -> tensor<10x10x10xf32> attributes {tac.device = "CPU", tac.interface_name = "func_0"} {
  %0 = "tfl.add"(%arg0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<10x10x10xf32>, tensor<10xf32>) -> tensor<10x10x10xf32>
  %1 = "tfl.mul"(%0, %arg1) {fused_activation_function = "RELU", tac.device = "CPU"} : (tensor<10x10x10xf32>, tensor<10xf32>) -> tensor<10x10x10xf32>

// CHECK:       "tfl.yield"(%{{.*}}) : (tensor<i1>) -> ()
// CHECK:     },  {
// CHECK:     ^bb0(%{{.*}}: tensor<*xi32>, %{{.*}}: tensor<*xf32>):
// CHECK:       "tfl.yield"(%{{.*}}, %{{.*}}) : (tensor<*xi32>, tensor<*xf32>) -> ()
// CHECK:     }) : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)

func.func @main(%arg0: tensor<i32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {

  %2 = func.call @fold_all_test(%arg0, %0, %1) : (tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x30x30x16xf32>
  func.return %2 : tensor<256x30x30x16xf32>
}

// ALL-LABEL: @fold_all_test
func.func @fold_all_test(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>) -> tensor<256x30x30x16xf32> {

// CHECK:     "tfl.yield"(%[[RES1]]) : (tensor<*xf32>) -> ()
// CHECK:   }) : (tensor<1xf32>) -> tensor<*xf32>
// CHECK:   return %0 : tensor<*xf32>
// CHECK: }
func.func @main(%arg0: tensor<1xf32>) -> tensor<*xf32> {
  %0 = "tf.While"(%arg0) {cond = @cond, body = @body, is_stateless = false} : (tensor<1xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}