# `tf._resource_arg_unique_id` argument attribute of the function
# @test_func_name0.

# CHECK:  func @main
# CHECK:    tf_executor.graph
# CHECK:      "tf.VarHandleOp"()
# CHECK:      "tf.LegacyCall"
# CHECK-SAME:   <{_disable_call_shape_inference = true, f = @test_func_name0}> {device = ""}
# CHECK:      tf_executor.fetch
# CHECK:    return
# CHECK:  func private @test_func_name0
# CHECK-SAME:   tf._resource_arg_unique_id = 0

```
### `-tf-executor-to-functional-conversion`

_Lifts tf_executor.island inner ops from a tf_executor.graph_

This pass converts tf_executor.graphs consisting of only tf_executor.islands and
a tf_executor.fetch into a sea of nodes consisting of TensorFlow Dialect ops by
lifting such ops out of a tf_executor.graph's tf_executor.islands. If V1 control
flow ops are present in a tf_executor.graph, an error will be returned.

// RUN: tf-opt -tf-executor-graph-pruning %s  | FileCheck %s --check-prefix=CONTROL

// CONTROL-LABEL: func @main
// CONTROL-NEXT:    return

// EXECUTOR-LABEL: func @main
// EXECUTOR-NEXT:    tf_executor.graph {
// EXECUTOR-NEXT:      tf_executor.fetch
// EXECUTOR-NEXT:    }
// EXECUTOR-NEXT:    return

func.func @main() {
  func.return

      tf_executor.yield %3 : tensor<i1>
    }
    %2:2 = tf_executor.island(%1#1) {
      %4 = "tf.opB"() : () -> tensor<f32>
      tf_executor.yield %4 : tensor<f32>
    }
    tf_executor.fetch %2#0 : tensor<f32>
  }
  func.return %0 : tensor<f32>
}

// CHECK-LABEL: @func2
func.func @func2(%arg0 : tensor<i1>) -> tensor<i1> {
  %0 = tf_executor.graph {
    %1:2 = tf_executor.island {

// tf-opt invocation.



// This test case exercises the "tf_executor.island wraps" syntax.
// When parsing it back, we should recover all 3 locations (the
// tf_executor.island, tf.Identity, and tf_executor.yield).

// CHECK-LABEL: "func.func"
// CHECK: sym_name = "island_one_op_all_locs_same"
// CHECK:    "tf_executor.graph"() ({
// CHECK-NEXT:      "tf_executor.island"() ({

func.func @nested_entry_functions() attributes {tf.entry_function = {}} {
  tf_executor.graph {
     %control = tf_executor.island wraps "tf.StatefulPartitionedCall"() {config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @func} : () -> ()
     tf_executor.fetch
  }
  func.return
}

func.func @func() attributes {tf.entry_function = {}} {
  func.return
}

func.func @main() {
  tf_executor.graph {
    %0 = tf_executor.island wraps "tf._TPUReplicate"() {computation = @foo, Tinputs = [], Tbroadcast_inputs = [], NumVariables = 0, Tguaranteed_constants = [], output_types = []} : () -> () loc("_TPUReplicate")
    tf_executor.fetch
  }
  func.return
}

func.func @foo() {
  tf_executor.graph {

  // CHECK-NEXT:   }
  // CHECK:      key: "value"
  // CHECK-NEXT: value {
  // CHECK-NEXT:   s: " 0\n\000\000"
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Placeholder"() {name = "dummy", dtype = "tfdtype$DT_INT32", value = "\200\n\00\00", listvalue = ["\20\0A"]} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

  %1 = "tf.Const"() {value = dense<3.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %2 = "tf.Const"() {value = dense<4.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %3 = tf_executor.graph {
    %res, %ctl = tf_executor.island {
      %3 = "tf_device.cluster"() ({

        // In the device region, check that the 3 constants are materialized and
        // remapped to the uses.
        // CHECK: tf_device.cluster

func.func @main() {
  tf_executor.graph {
    %0 = tf_executor.island wraps "tf._TPUReplicate"() {computation = @foo, Tinputs = [], Tbroadcast_inputs = [], NumVariables = 0, Tguaranteed_constants = [], output_types = []} : () -> () loc("_TPUReplicate")
    tf_executor.fetch
  }
  func.return
}

func.func @foo() {
  tf_executor.graph {