// - We use a KMS -> There must be non-empty key ID and a KMS data key.
	// - We use a K/V -> There must be no key ID and no KMS data key.
	// Otherwise, the caller has passed an invalid argument combination.
	if keyID == "" && len(kmsKey) != 0 {
		logger.CriticalIf(context.Background(), errors.New("The key ID must not be empty if a KMS data key is present"))
	}
	if keyID != "" && len(kmsKey) == 0 {

		}
	}
}

func TestS2CompressReader(t *testing.T) {
	tests := []struct {
		name    string
		data    []byte
		wantIdx bool
	}{
		{name: "empty", data: nil},
		{name: "small", data: []byte("hello, world!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")},
		{name: "large", data: bytes.Repeat([]byte("hello, world"), 1000000), wantIdx: true},
	}

	for _, tt := range tests {

    protected Queue<Cipher> decryptoQueue = new ConcurrentLinkedQueue<>();

    public byte[] encrypto(final byte[] data) {
        final Cipher cipher = pollEncryptoCipher();
        byte[] encrypted;
        try {
            encrypted = cipher.doFinal(data);
        } catch (final IllegalBlockSizeException e) {
            throw new IllegalBlockSizeRuntimeException(e);

        schedulerPager.clear();
        return asHtml(path_AdminScheduler_AdminSchedulerJsp).renderWith(data -> {
            searchPaging(data, form);
        });
    }

    protected void searchPaging(final RenderData data, final SearchForm form) {
        RenderDataUtil.register(data, "scheduledJobItems", scheduledJobService.getScheduledJobList(schedulerPager)); // page navi

        // restore from pager

}

// Returns streaming bitrot reader implementation.
func newStreamingBitrotReader(disk StorageAPI, data []byte, volume, filePath string, tillOffset int64, algo BitrotAlgorithm, shardSize int64) *streamingBitrotReader {
	h := algo.New()
	return &streamingBitrotReader{
		disk:       disk,
		data:       data,
		volume:     volume,
		filePath:   filePath,

    return new AbstractList<T>() {
      ArrayList<T> data = new ArrayList<>();

      @Override
      public int size() {
        return max(0, data.size() + delta);
      }

      @Override
      public T get(int index) {
        return data.get(index);
      }

      @Override
      public T set(int index, T element) {
        return data.set(index, element);
      }

      @Override

  dlm_tensor->dl_tensor.shape = shape_arr->data();
  // There are two ways to represent compact row-major data
  // 1) nullptr indicates tensor is compact and row-majored.
  // 2) fill in the strides array as the real case for compact row-major data.
  // Here we choose option 2, since some frameworks didn't handle the strides
  // argument properly.
  dlm_tensor->dl_tensor.strides = stride_arr->data();

  dlm_tensor->dl_tensor.byte_offset =

@needs_py39
def test_post_body(client: TestClient):
    data = {"2": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 200, response.text
    assert response.json() == data


@needs_py39
def test_post_invalid_body(client: TestClient):
    data = {"foo": 2.2, "3": 3.3}
    response = client.post("/index-weights/", json=data)
    assert response.status_code == 422, response.text

 * underlying (implementation specific) data structures to be shared.
 *
 * <p>The size of the data structure used by ArrayBasedCharEscaper and ArrayBasedUnicodeEscaper is
 * proportional to the highest valued character that has a replacement. For example a replacement
 * map containing the single character '{@literal \}u1000' will require approximately 16K of memory.

Pydantic models have a `.dict()` method that returns a `dict` with the model's data.

So, if we create a Pydantic object `user_in` like:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

and then we call:

```Python
user_dict = user_in.dict()
```

we now have a `dict` with the data in the variable `user_dict` (it's a `dict` instead of a Pydantic model object).