%cst = "tf.Const"() {device = "", value = dense<[[[[-0.315365672, 0.27481091], [0.0901821703, -0.382271349], [-0.105572946, -0.354302853]], [[-0.47703138, -0.307006568],...

module attributes {tf.versions = {bad_consumers = [], min_consumer = 12 : i32, producer = 29 : i32}} {
  func.func @main() {
    tf_executor.graph {
      %0 = tf_executor.island wraps "tf.PartitionedCall"() {Tin = [], Tout = [], config = "", config_proto = "", device = "", executor_type = "", f = @foo, name = "Call_foo"} : () -> ()
      tf_executor.fetch
    }
    func.return
  }
  func.func @foo() {

    %1 = "tf.Reshape"(%arg1, %0) : (tensor<19x10xf32>, tensor<2xi64>) -> tensor<?x?xf32>

    // CHECK: %[[RESULT:.*]] = mhlo.constant dense<[10, 19]>
    %2 = "tf.PartitionedCall"(%1) {config = "", config_proto = "", executor_type = "", f = @get_shape} : (tensor<?x?xf32>) -> (tensor<?xi64>)

    // CHECK: return %[[RESULT]]
    func.return %2 : tensor<?xi64>
  }

  // CHECK: _xla_original_oc_node_name = [[NODE_NAME1:.*]], _xla_token_input_nodes = ["_xla_token_arg_node"]
  %0 = "tf.PartitionedCall"(%arg0) {config = "", config_proto = "", executor_type = "", f = @PartitionedCall3} : (tensor<i32>) -> (tensor<i32>)
  // CHECK-NOT: _xla_token_input_nodes
  %1 = "tf.PartitionedCall"(%0) {config = "", config_proto = "", executor_type = "", f = @IdentityFunc} : (tensor<i32>) -> (tensor<i32>)

func.func @serving_default(%arg0: tensor<1x3x4x3xf32>) -> (tensor<1x2x2x2xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "serving_default_input_tensor:0", outputs = "PartitionedCall:0"}} {

    : (tensor<!tf_type.resource>, tensor<i1>) -> tensor<!tf_type.resource>
  // CHECK: "tf.PartitionedCall"
  // CHECK-SAME: f = @callee_stack_decomposed
  %call2 = "tf.PartitionedCall"(%stack, %arg0) {f = @callee, config = "", config_proto = "", executor_type = ""}
    : (tensor<!tf_type.resource>, tensor<i1>) -> tensor<!tf_type.resource>
  // CHECK: "tf.Slice"

    %cst_7 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %0 = "tf.PartitionedCall"(%arg0, %cst_1, %cst_2) {config = "", config_proto = "", executor_type = "", f = @quantize_i8} : (tensor<1x3x4x3xf32>, tensor<f32>, tensor<i32>) -> tensor<1x3x4x3xi8>

// PartitionedCall are placed on the host, a producer that produces them on the
// device will incur a D2H copy, even if the PartitionedCall is not executed
// (i.e. even if we choose to execute the XLA compiled computation via _XlaRun).
// To prevent this, we add control dependencies to make the int32 input edges
// into the PartitionedCall dead.  With this change the D2H copy only happens if

    func.return %2 : tensor<*xi32>
  }
}

// -----

// CHECK: module attributes {tfl._legalize_tfl_variables = true}
module {
  func.func @main() -> tensor<*xi32> {
    %0 = "tf.PartitionedCall"() {f = @f, config = "", config_proto = "", executor_type = ""}
      : () -> tensor<*xi32>
    func.return %0 : tensor<*xi32>
  }
  func.func @f() -> tensor<*xi32> {

    : (tensor<!tf_type.resource>) -> tensor<!tf_type.resource>
  // CHECK: "tf.PartitionedCall"(%[[VAR]], %[[GVAR1]], %[[GVAR2]])
  // CHECK-SAME: f = @callee_tensorarray_decomposed
  %call2 = "tf.PartitionedCall"(%call) {f = @callee, config = "", config_proto = "", executor_type = ""}
    : (tensor<!tf_type.resource>) -> tensor<!tf_type.resource>