%1 = "tf.Mul"(%arg1, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %2 = "tf.Div"(%arg1, %arg1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>

  // CHECK: "tf.If"{{.+}}else_branch = @tf.IfRegion_else{{.+}}then_branch = @tf.IfRegion_then
  %3 = "tf.IfRegion"(%arg0) ({
     %4 = "tf.Select"(%arg0, %0, %1) : (tensor<i1>, tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
     "tf.Yield"(%4) : (tensor<2xf32>) -> ()

"TANH", input_to_cell_intermediate = tensor<0xf32>, input_to_forget_intermediate = tensor<0xf32>, input_to_input_intermediate = tensor<0xf32>, input_to_output_intermediate = tensor<0xf32>, proj_clip = 0.000000e+00 : f32, time_major = false} : (tensor<?x?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>, tensor<?xf32>,...

    func.return %0#0 : tensor<8x8x8x8xf32>
  }

  // CHECK-LABEL: fusedBatchNormV3_training

// CHECK-NEXT:   "tfl.yield"(%[[MY_CUSTOM]]) : (tensor<4xf32>) -> ()
// CHECK-NEXT: }) {fused_activation_function = "RELU", int_attr = 2 : i32} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
// CHECK-NEXT: %[[CUSTOM_2:.*]] = "tfl.custom_tf"(%[[MUL]], %[[CST]]) ({
// CHECK-NEXT: ^bb0(%arg1: tensor<4xf32>, %arg2: tensor<4xf32>):

// CHECK: tf.Div(tensor<4xf32>, tensor<4xf32>) -> (tensor<4xf32>) : {name = "div"}

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
  %0 = "tfl.pseudo_const" () {name = "Const", value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32>
  %1 = "tfl.mul"(%arg0, %0) {fused_activation_function = "NONE", name = "mul"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>

// CHECK-SAME: (%[[ARG0:.*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
func.func @testReadVariableOpColocated(%arg0: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
  // CHECK:      tf_device.replicate
  // CHECK-SAME: (%[[ARG0]] as %[[RI_0:[a-z0-9]*]]: tensor<*x!tf_type.resource<tensor<4xf32>>>)
  tf_device.replicate(%arg0 as %arg1: tensor<*x!tf_type.resource<tensor<4xf32>>>) {
    _mirrored_variable_indices = [0],

  %4 = "tf.AddN"(%1, %1) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  %5 = "tf.AddN"(%arg0, %1, %0) : (tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  func.return %2, %3, %4, %5: tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>
}

// CHECK-LABEL: func @addNWithZerosInt

func.func @main(%arg0: tensor<4xf32>) -> tensor<4xf32> {
  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.squared_difference"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("squared_difference")
  %2 = "tfl.mul"(%0, %1) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("mul")

// CHECK-LABEL: func @init
func.func @init(%arg0: tensor<4xf32>) -> tensor<7xf32> {
    %0 = builtin.unrealized_conversion_cast to tensor<7xf32>
    return %0 : tensor<7xf32>
}

// CHECK-LABEL: func @next
func.func @next(%arg0: tensor<7xf32>, %arg1: tensor<3xf32>) -> tensor<6xf32> {
    %0 = builtin.unrealized_conversion_cast to tensor<6xf32>
    return %0 : tensor<6xf32>
}

// CHECK-LABEL: func @finalize

    %outputs_6, %control_7 = tf_executor.island wraps "tf.If"(%outputs_2, %arg2, %arg3) {else_branch = @cond_false, is_stateless = true, then_branch = @cond_true} : (tensor<i1>, tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("StatelessIf")
    tf_executor.fetch %outputs_4, %outputs_6 : tensor<4xf32>, tensor<4xf32>
  }
  func.return %0#0, %0#1 : tensor<4xf32>, tensor<4xf32>
}