%cst = "tf.Const"() {device = "", value = dense<[7.72826624, 8.8264122, 3.64885974]> : tensor<3xf32>} : () -> tensor<3xf32>

    %i8_max = "tf.Const"() {value = dense<127> : tensor<i8>} : () -> tensor<i8>

    %clip_min = "tf.PartitionedCall"(%i8_min, %out_scale, %out_zp) {
        config = "", config_proto = "", executor_type = "", f=@internal_dequantize_i8_fn
      } : (tensor<i8>, tensor<*xf32>, tensor<*xi32>) -> tensor<*xf32>
    %clip_max = "tf.PartitionedCall"(%i8_max, %out_scale, %out_zp) {

    func.return
  }
}

// -----
// Test stateful and stateless partitioned calls.
// CHECK-LABEL: func @f
func.func @f() {
  // CHECK: "tf.PartitionedCall"() <{config = "",  config_proto = "", executor_type = "", f = @g}> : () -> ()
  "tf.PartitionedCall"() {config = "",  config_proto = "", executor_type = "", f = @g} : () -> ()

    %val = "tf.PartitionedCall"(%handle) {config = "", config_proto = "", executor_type = "", f = @f_callee} : (tensor<!tf_type.resource<tensor<0xf32>>>) -> (tensor<0xf32>)
    func.return %val : tensor<0xf32>
  }

  // CHECK: func private @f_callee() -> tensor<0xf32>
  func.func private @f_callee(%arg0: tensor<*x!tf_type.resource>) -> tensor<0xf32> {

    %0 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @serving_default} : (tensor<?x!tf_type.string>) -> (tensor<*xi64>)
    %1 = "tf.Identity"(%0) : (tensor<*xi64>) -> tensor<*xi64>
    return %1 : tensor<*xi64>
  }

    %cst_5 = "tf.Const"() {value = dense<0> : tensor<512xi32>} : () -> tensor<512xi32>
    %0 = "tf.PartitionedCall"(%arg0, %cst_0, %cst_1) {config = "", config_proto = "", executor_type = "", f = @quantize_i8} : (tensor<1x2240x2240x3xf32>, tensor<f32>, tensor<i32>) -> tensor<1x2240x2240x3xi8>

module {
  func.func @wrap_composite_func(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
    %0 = "tf.PartitionedCall"(%arg0, %arg1) <{f = @composite_conv2d_with_relu6_fn}> {_tfl_quant_trait = "fully_quantizable"}
          : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>
  }

// -----

module {
  func.func @matmul(%arg0: tensor<1x2x2x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2x1024xf32>} : () -> tensor<2x1024xf32>
    %1 = "tf.PartitionedCall"(%arg0, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_matmul_fn} : (tensor<1x2x2x3xf32>, tensor<2x1024xf32>) -> tensor<*xf32>
    func.return %1: tensor<*xf32>
  }

    %0 = "tf.PartitionedCall"(%arg0, %cst_1) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_depthwise_conv2d_fn} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

      ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]
    } : (tensor<*xf32>) -> tensor<*xf32>
    %3 = "tf.Reshape"(%2, %cst) {device = ""} : (tensor<*xf32>, tensor<2xi32>) -> tensor<*xf32>
    %4 = "tf.PartitionedCall"(%3, %cst_1, %cst_2) {
      _tfl_quant_trait = "fully_quantizable", config = "", config_proto = "",
      executor_type = "", f = @composite_matmul_with_bias_fn_1