%0 = "tf.StatefulPartitionedCall"(%arg0) <{
      config = "", config_proto = "", executor_type = "", f = @some_func
    }> {
      _collective_manager_ids = [], device = ""
    } : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    // CHECK-SKIP: tf.StatefulPartitionedCall
    // CHECK-NOSKIP: call @some_func
    // CHECK-NOSKIP-NOT: tf.StatefulPartitionedCall
    %1 = "tf.PartitionedCall"(%0) <{

    // The order of these functions is also significant.
    // CHECK: {{.*StatefulPartitionedCall.* f = @_func_non_tpu.*}}
    // CHECK-NEXT: {{.*StatefulPartitionedCall.* f = @_func_sc_forward.*}}
    // CHECK-NEXT: {{.*StatefulPartitionedCall.* f = @_func_core_tpu.*}}
    // CHECK-NEXT: {{.*StatefulPartitionedCall.* f = @_func_sc_backward.*}}
    // CHECK-NEXT: return
    // metadata ops

    // CHECK: {{.*StatefulPartitionedCall.* f = @while_cond.*}}
    // CHECK: {{.*StatefulPartitionedCall.* f = @non_tpu.*}}
    // CHECK: {{.*StatefulPartitionedCall.* f = @start_step_0.*}}
    // CHECK: {{.*StatefulPartitionedCall.* f = @while_cond.*}}
    // CHECK: {{.*StatefulPartitionedCall.* f = @non_tpu.*}}
    // CHECK: {{.*StatefulPartitionedCall.* f = @start_step_1.*}}

      // CHECK: %[[CST:.*]] = "tf.Const"
      %result = "tf.StatefulPartitionedCall"() {config = "", config_proto = "", executor_type = "", f = @simple_callee} : () -> tensor<2xi32>

      // Multi block regions may not.
      // CHECK-NEXT: %[[CALL:.*]] = "tf.StatefulPartitionedCall"

// CHECK: tf_device.cluster
// CHECK-NEXT: tf.StatefulPartitionedCall
// CHECK-NEXT: tf_device.return
// CHECK: _cluster_outlined_function_name = "stateful_pcall_func"
// CHECK-SAME: allow_soft_placement = true
// CHECK: tf.Const
// CHECK: tf.Add
func.func @xla_must_compile_true(%arg0: tensor<i32>) -> tensor<i32> attributes {tf.entry_function = {}} {

  %0 = "tf.StatefulPartitionedCall"(%arg0) {_xla_compile_device_type = "CPU", config = "", config_proto = "", device = "/device:CPU:0", executor_type = "", f = @func} : (tensor<i32>) -> (tensor<i32>)
  func.return %0 : tensor<i32>
}

func.func @entry_func_2(%arg0: tensor<i32>) -> tensor<i32> attributes {tf_saved_model.initializer_type = ""} {

    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
        }
        float_val: 0.0
      }
    }
  }
}
node {
  name: "statefulpartitionedcall"
  op: "StatefulPartitionedCall"
  input: "const"
  input: "args_1"
  attr {
    key: "Tin"
    value {
      list {
        type: DT_FLOAT
        type: DT_RESOURCE
      }
    }
  }

// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [  ],
// CHECK-NEXT:  subgraphs: [ {
// CHECK-NEXT:    tensors: [ {
// CHECK-NEXT:      shape: [ 5 ],
// CHECK-NEXT:      buffer: 1,
// CHECK-NEXT:      name: "StatefulPartitionedCall:1",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:      },
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    } ],
// CHECK-NEXT:    inputs: [  ],
// CHECK-NEXT:    outputs: [ 0 ],

    %cst = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %cst_0 = arith.constant dense<1.0> : tensor<5x3xf32>

// CHECK-NEXT:      buffer: 6,
// CHECK-NEXT:      name: "StatefulPartitionedCall:0",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY:
// CHECK-NEXT:      },
// CHECK-NEXT:      shape_signature: [ -1, 5 ],
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    }, {
// CHECK-NEXT:      shape: [ 1, 5 ],
// CHECK-NEXT:      buffer: 7,
// CHECK-NEXT:      name: "StatefulPartitionedCall:1",
// CHECK-NEXT:      quantization: {
// CHECK-EMPTY: