%weight = "tf.Const"() {value = dense<2.000000e+00> : tensor<2x3x3x2xf32>} : () -> tensor<2x3x3x2xf32>
    %1 = "tf.PartitionedCall"(%arg0, %weight) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_conv2d_fn_1} : (tensor<1x2x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

    %0 = "tf.PartitionedCall"(%arg0, %cst, %cst_0) {config = "", config_proto = "", executor_type = "", f = @composite_conv2d_with_bias_and_relu6_fn_2} : (tensor<1x2x2x3xf32>, tensor<2x2x3x2xf32>, tensor<2xf32>) -> tensor<*xf32> loc(callsite("test@conv"("Conv2D") at "QuantizationUnit(\12\06Conv2D\1a\04conv)"))

// CHECK-SAME: ([[ARG0:%.*]]: tensor<i32>)
func.func @testTransitiveIdentity(%arg0: tensor<i32>) {
  // CHECK:      "tf_device.cluster"
  // CHECK:      "tf.PartitionedCall"([[ARG0]])
  // CHECK-SAME: f = @callee0
  %0 = "tf_device.cluster"() ({
    %1 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @callee0} : (tensor<i32>) -> tensor<i32>
    tf_device.return %1 : tensor<i32>
  }) : () -> tensor<i32>

// TF-DAG: %[[pc_0:.*]] = "tf.PartitionedCall"(%arg0, %[[cst_4]], %[[cst_5]]) <{config = "", config_proto = "", executor_type = "", f = @quantize_i8}
// TF-DAG: %[[pc_1:.*]] = "tf.PartitionedCall"(%[[pc_0]], %[[cst_2]], %[[cst_4]], %[[cst_5]], %[[cst_8]], %[[cst_9]], %[[cst_6]], %[[cst_5]]) <{config = "", config_proto = "", executor_type = "", f = @quantized_matmul_fn_1}

module {
// CHECK-LABEL: func @func0
  func.func @func0(%arg0: tensor<i1>) -> tensor<f32> {
    %0 = tf_executor.graph {
// CHECK-NOT: PartitionedCall
// CHECK: "tf.opA"
      %outputs, %control = tf_executor.island wraps "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @_tpu_v1_compat_outlined::@_tpu_v1_compat_outlined_func0} : (tensor<i1>) -> tensor<i1>

}

// CHECK-LABEL: @float_conv_3
// CHECK:  "tf.PartitionedCall"
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_6
// CHECK:  "tf.PartitionedCall"
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_5

// CHECK-LABEL: @float_conv_1
// CHECK:  "tf.PartitionedCall"
// CHECK-SAME: f = @composite_conv2d_with_bias_and_relu6_fn_2
// CHECK:  "tf.PartitionedCall"

// CHECK-DAG: %[[w_quant:.*]] = "tf.Const"() <{value = dense<{{\[\[\[\[}}40, 20]
// CHECK-DAG: {{\[\[\[}}-87, -42]

// CHECK: %[[quantize:.*]] = "tf.PartitionedCall"(%arg0, %[[in_scale]], %[[in_zp]])
// CHECK-SAME: f = @quantize_i8
// CHECK: %[[conv_quant:.*]] = "tf.PartitionedCall"(%[[quantize]], %[[w_quant]], %[[b_quant]],
// CHECK-SAME: %[[in_scale]], %[[in_zp]], %[[w_scale]], %[[w_zp]],

func.func @main() {
  tf_executor.graph {
    // CHECK: node {
    // CHECK-NEXT: name: "tf.PartitionedCall"
    // CHECK-NEXT: op: "PartitionedCall"
    // CHECK:   func {
    // CHECK:     name: "foo"
    // CHECK:   }
    // CHECK: }
    %0 = tf_executor.island wraps "tf.PartitionedCall"() {Tin = [], Tout = [], config = "", config_proto = "", device = "", executor_type = "", f = @foo, name = "Call_foo"} : () -> ()

  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @f_callee} : (tensor<!tf_type.resource<tensor<f32>>>) -> (tensor<f32>)
    func.return %val : tensor<f32>
  }

  func.func private @f_callee(%arg0: tensor<*x!tf_type.resource>) -> tensor<f32>  {

    %cst = "tf.Const"() {value = dense<127> : tensor<2x1024xi8>} : () -> tensor<2x1024xi8>
    %0 = "tf.Identity"(%cst) : (tensor<2x1024xi8>) -> tensor<2x1024xi8>
    %1 = "tf.PartitionedCall"(%0) {config = "", config_proto = "", executor_type = "", f = @composite_dequantize_uniform} : (tensor<2x1024xi8>) -> tensor<2x1024xbf16>