}

	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()

  dlm_tensor->dl_tensor.dtype = tf_dlm_type;

  std::vector<int64_t>* shape_arr = &tf_dlm_tensor_ctx->shape;
  std::vector<int64_t>* stride_arr = &tf_dlm_tensor_ctx->strides;
  shape_arr->resize(ndim);
  stride_arr->resize(ndim, 1);
  for (int i = 0; i < ndim; i++) {
    (*shape_arr)[i] = tensor->dim_size(i);
  }
  for (int i = ndim - 2; i >= 0; --i) {
    (*stride_arr)[i] = (*shape_arr)[i + 1] * (*stride_arr)[i + 1];

  }

  public void testBuilder_presize_zero() {
    ImmutableDoubleArray.Builder builder = ImmutableDoubleArray.builder(0);
    builder.add(5.0);
    ImmutableDoubleArray array = builder.build();
    assertThat(array.asList()).containsExactly(5.0);
  }

  public void testBuilder_presize_negative() {
    try {
      ImmutableDoubleArray.builder(-1);
      fail();

 * 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>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

 * 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>Unlike {@code java.util.HashSet}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

      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.
 *

 * 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>Unlike {@code java.util.HashMap}, iteration is only proportional to the actual {@code size()},
 * which is optimal, and <i>not</i> the size of the internal hashtable, which could be much larger

 * 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.
 *