}

	stream, err := sio.AES_256_GCM.Stream(key)
	if err != nil {
		return err
	}
	// Zero nonce, we only use each key once, and 32 bytes is plenty.
	nonce := make([]byte, stream.NonceSize())
	encr := stream.DecryptReader(r, nonce, nil)
	_, err = io.Copy(w, encr)
	if err == nil {
		fmt.Println(okMsg)
	}
	return err

			HTTPStatusCode: http.StatusBadRequest,
			APICode:        "KMS.InternalException",
			Err:            errors.New("invalid algorithm: " + algorithm),
		}
	}

	nonce, err := sioutil.Random(aead.NonceSize())
	if err != nil {
		return DEK{}, err
	}

	plaintext, err := sioutil.Random(32)
	if err != nil {
		return DEK{}, err
	}
	associatedData, _ := context.MarshalText()

      func_inputs.reserve(inputs.size());
      TF_RETURN_IF_ERROR(
          CreateParamsForInputs(func_ctx.get(), inputs, &func_inputs));
      std::vector<AbstractTensorHandle*> model_outputs;
      model_outputs.resize(outputs.size());
      TF_RETURN_IF_ERROR(model(func_ctx.get(), absl::MakeSpan(func_inputs),
                               absl::MakeSpan(model_outputs)));
      for (auto func_input : func_inputs) {
        func_input->Unref();

 * function doing a good job of distributing the elements to the buckets to a distribution not far
 * from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>As compared with {@link java.util.LinkedHashMap}, this structure places significantly reduced
 * load on the garbage collector by only using a constant number of internal objects.
 *

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>This implementation consumes significantly less memory than {@code java.util.LinkedHashSet} or
 * even {@code java.util.HashSet}, and places considerably less load on the garbage collector. Like

void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {
  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}
TF_AbstractTensor* TF_OutputListGet(TF_OutputList* o, int i) {
  return wrap(unwrap(o)->outputs[i]);
}

 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>This implementation consumes significantly less memory than {@code java.util.LinkedHashSet} or
 * even {@code java.util.HashSet}, and places considerably less load on the garbage collector. Like