try {
              new CountDownLatch(1).await(); // the interrupt will wake us up
            } catch (InterruptedException ie) {
              // continue
            }
            LockSupport.unpark(Thread.currentThread()); // simulate a spurious wakeup.
            return null;
          }

          @Override
          boolean isDone() {
            return false;
          }

            try {
              new CountDownLatch(1).await(); // the interrupt will wake us up
            } catch (InterruptedException ie) {
              // continue
            }
            LockSupport.unpark(Thread.currentThread()); // simulate a spurious wakeup.
            return null;
          }

          @Override
          boolean isDone() {
            return false;
          }

	Files     uint64
	Ffree     uint64
	Fsid      Fsid64_t
	Vfstype   int32
	Fsize     uint64
	Vfsnumber int32
	Vfsoff    int32
	Vfslen    int32
	Vfsvers   int32
	Fname     [32]uint8
	Fpack     [32]uint8
	Name_max  int32
	Pad_cgo_0 [4]byte
}

type Fsid64_t struct {
	Val [2]uint64
}

type StTimespec_t struct {
	Sec       int64
	Nsec      int32
	Pad_cgo_0 [4]byte
}

}
```

but it doesn't have the `id` field.

So we create a new `dict`, that contains the key-value pairs from `note.dict()` with:

```Python
{**note.dict()}
```

`**note.dict()` "unpacks" the key value pairs directly, so, `{**note.dict()}` would be, more or less, a copy of `note.dict()`.

And then, we extend that copy `dict`, adding another key-value pair: `"id": last_record_id`:

```Python

# CHECK-SAME:   [[ARG:%.*]]: tensor<*xf32> {tf_saved_model.index_path = ["x"]}

# CHECK: [[shape:%.*]] = "tf.Shape"([[ARG]])
# CHECK-NEXT: [[batch_size:%.*]] = "tf.StridedSlice"([[shape]],
# CHECK-NEXT: [[result:%.*]] = "tf.Pack"([[batch_size]],
# CHECK-NEXT: return [[result]] : tensor<2xi32>


def Test():

  x = tf.placeholder(dtype=tf.float32, shape=[None])
  batch_size = tf.shape(x)[0]
  r = tf.convert_to_tensor([batch_size, 1])

	Files     uint32
	Ffree     uint32
	Fsid      Fsid_t
	Vfstype   int32
	Fsize     uint32
	Vfsnumber int32
	Vfsoff    int32
	Vfslen    int32
	Vfsvers   int32
	Fname     [32]uint8
	Fpack     [32]uint8
	Name_max  int32
}

  }
};

std::unique_ptr<TargetHardwareOperation> CreateBasicOpNoCost() {
  return std::make_unique<NNAPIBasicSupportedOpNoCost>();
}

// Currently used for these ops:
// tfl.concatenation / tfl.reshape / tfl.pack
class NNAPIConcatOp : public TargetHardwareOperation {
  double GetOpCost(mlir::Operation* op) const override {
    int64_t count;
    if (ArithmeticCountUtilHelper::GetInputTensorTotalSize(op, &count))

	Files     uint64
	Ffree     uint64
	Fsid      Fsid64_t
	Vfstype   int32
	Fsize     uint64
	Vfsnumber int32
	Vfsoff    int32
	Vfslen    int32
	Vfsvers   int32
	Fname     [32]uint8
	Fpack     [32]uint8
	Name_max  int32
	_         [4]byte
}

				TypeUrl:       model.EndpointType,
				VersionInfo:   "v1",
				ResponseNonce: "nonce",
				ResourceNames: []string{"my-resource"},
			},
			response: true,
		},
		{
			name: "nack",
			proxy: &TestProxy{
				WatchedResources: map[string]*WatchedResource{
					model.ClusterType: {
						NonceSent: "nonce",
					},
				},
			},
			request: &discovery.DiscoveryRequest{

// go generate gen.go
// Code generated by the command above; DO NOT EDIT.

package hpack

var staticTable = &headerFieldTable{
	evictCount: 0,
	byName: map[string]uint64{
		":authority":                  1,
		":method":                     3,
		":path":                       5,
		":scheme":                     7,
		":status":                     14,
		"accept-charset":              15,
		"accept-encoding":             16,