func.func @QuantizedCatsAddRequantsTest(%arg0: tensor<1x1xf32>, %arg1: tensor<1x1xf32>, %arg2: tensor<1x1xf32>, %arg3: tensor<1x1xf32>) -> (tensor<1x4xf32>, tensor<1x3xf32>) {
  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[-0.440728068, 0.189515018]> : tensor<2xf32>} : (tensor<1x1xf32>) -> tensor<1x1xf32>
  %1 = "quantfork.stats"(%arg1) {layerStats = dense<[-0.154693216, 0.26483655]> : tensor<2xf32>} : (tensor<1x1xf32>) -> tensor<1x1xf32>

// CHECK-SAME: (%[[ARG0:.*]]: tensor<3x4xf32>)
func.func @expm1(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
  %0 = "tf.Expm1"(%arg0) : (tensor<3x4xf32>) -> tensor<3x4xf32>
  func.return %0 : tensor<3x4xf32>
  // CHECK: %[[ONE:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
  // CHECK: %[[EXP:.*]] = "tf.Exp"(%[[ARG0]]) : (tensor<3x4xf32>) -> tensor<3x4xf32>

// CHECK-NEXT: }

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")
  %1,%control_1 = tfl.control_node controls "tfl.sub" (%arg0, %0) {fused_activation_function = "RELU6"} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32> loc("sub")
  %2 = "arith.constant" () {value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("SameNameAsOutput")

  // CHECK-LABEL: @xla_svd
  func.func @xla_svd(%arg0: tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>) {
    // CHECK-NOT: XlaSvd
    %s, %u, %v = "tf.XlaSvd"(%arg0) {max_iter = 1, epsilon = 1.0E-09 : f32, precision_config = ""} : (tensor<1x1xf32>) -> (tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>)
    func.return %s, %u, %v : tensor<1xf32>, tensor<1x1xf32>, tensor<1x1xf32>
  }

  func.func @identity(%arg0: f32) -> f32 {

  // CHECK-NEXT:     [[SCALAR:%.*]] = "tfl.pseudo_const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>
  // CHECK-NEXT:     [[ADD:%.*]] = tfl.add([[SCALAR]], [[SUB]]) {fused_activation_function = "NONE"} : (tensor<i32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  // CHECK-NEXT:     return [[ADD]] : tensor<3x2xi32>

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")

} {
  func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32>
    attributes {tf.entry_function = {inputs = "input", outputs = "SameNameAsOutput"}} {
  ^bb0(%arg0: tensor<3x2xi32>):
    %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
    %1 = "tfl.sub" (%arg0, %0) {fused_activation_function = "NONE"} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
    func.return %1 : tensor<3x2xi32>
  }

func.func @main(tensor<3x2xi32>) -> tensor<3x2xi32> {
^bb0(%arg0: tensor<3x2xi32>):
  %0 = "arith.constant"() {name = "Const2", value = dense<10> : tensor<i32>} : () -> tensor<i32>
  %1 = "tfl.add"(%0, %arg0) {fused_activation_function = "NONE", name = "add"} : (tensor<i32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  func.return %1 : tensor<3x2xi32>

    //expected-error@below {{'tf.ReluOp' op is not a supported user of RestoreV2Op}}
    %2 = "tf.ReluOp"(%0) : (tensor<3x1xf32>) -> tensor<3x1xf32>
    %1 = "tf.VarHandleOp"() <{container = "x", shared_name = "y"}> : () -> tensor<!tf_type.resource<tensor<3x1xf32>>>
    "tf.AssignVariableOp"(%1, %2) : (tensor<!tf_type.resource<tensor<3x1xf32>>>, tensor<3x1xf32>) -> ()
    return
  }
}

    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "y"}> : () -> tensor<!tf_type.resource<tensor<3x1xf32>>>
    %array_key, %tensor = "tf.IfrtLoadVariable"(%0) <{used_by_host = true}> : (tensor<!tf_type.resource<tensor<3x1xf32>>>) -> (tensor<!tf_type.string>, tensor<3x1xf32>)
    %1 = "tf.MatMul"(%arg0, %tensor) : (tensor<1x3xf32>, tensor<3x1xf32>) -> tensor<1x1xf32>

// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>