%2 = "tfl.add"(%arg0, %arg3) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %3 = "tfl.pack"(%1, %2) {axis = 0 : i32, per_device_costs = {CPU = 2.0 : f32, GPU = -1.0 : f32}, values_count = 2 : i32, tac.device = "CPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>

  tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.ReadVariableOp"(%arg0) {device = ""} : (tensor<*x!tf_type.resource<tensor<8xf32>>>) -> tensor<8xf32>

// CHECK: while_1([[ARG0:%[^ :]*]]: tensor<i32>, [[ARG1:%[^ :]*]]: tensor<1xf32>)
func.func @while_1(%arg0: tensor<i32>, %arg1: tensor<1xf32>) -> tensor<1xf32> {
  // CHECK: [[CST:%[^ ]*]] = arith.constant dense<1> : tensor<i32>
  // CHECK: "tfl.while"([[ARG0]], [[ARG1]])
  // CHECK: (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)
  %0:2 = "tfl.while"(%arg0, %arg1) (
    // cond
    {

    "tf.Yield"(%2) : (tensor<i1>) -> ()
  }, {
  ^body(%barg0: tensor<*xf32>):
    %2 = "tf.SomeOp"(%barg0) : (tensor<*xf32>) -> tensor<*xf32>
    "tf.Yield"(%2) : (tensor<*xf32>) -> ()
  }) {is_stateless = false, shape_invariant} : (tensor<4xf32>) -> (tensor<*xf32>)

  func.return %0, %1 : tensor<*xf32>, tensor<*xf32>
}

// Test that both passes drop the shape_invariant attribute from
// While/WhileRegion ops within a cluster.

    %1 = "tf.BiasAdd"(%0, %arg2) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<2xf32>) -> tensor<*xf32>
    %2 = "tf.Relu6"(%1) : (tensor<*xf32>) -> tensor<*xf32>
    func.return %2 : tensor<*xf32>
  }

// CHECK-LABEL: func @conv_with_single_layer

// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%arg0

// CHECK-NEXT:   "tf.LayerNorm"(%arg4, %arg5, %arg6, %arg7) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>
// CHECK-NEXT:   "tfl.yield"
// CHECK-NEXT: }) {_tfl_quant_trait = "fully_quantizable", device = ""} :

    %1 = "tf.BiasAdd"(%0, %cst_0) {data_format = "NHWC", device = ""} : (tensor<*xf32>, tensor<6xf32>) -> tensor<*xf32>
    func.return %1: tensor<*xf32>
  }
  func.func private @composite_depthwise_conv2d_fn(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> attributes {tf_quant.composite_function} {

// 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>
}

// 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> {

  %0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.svdf"(%arg0, %arg1, %arg2, %arg3, %0) {fused_activation_function = "RELU", rank = 2 : i32} : (tensor<4xf32>, tensor<4xi8>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %1 : tensor<4xf32>