def METHOD_NAME(self):
    # Generating random numbers from a normal distribution for the weights and biases of the current and prev layer
    np.random.seed(1)
    total = 2 * 1 * 2 * 2
    weight = np.array(np.random.randn(total))
    weight_sz = np.array([2, 1, 2, 2])
    prev_layer_bias = np.array(np.random.randn(2))
    gamma = np.array(np.random.randn(2))
    beta = np.array(np.random.randn(2))
    curr_layer_bias = np.array(np.random.randn(2))
    prev_layer_params = libpymo.LayerParams()
    curr_layer_params = libpymo.LayerParams()
    prev_layer_bn_params = libpymo.BNParamsHighBiasFold()
    prev_layer_params.bias = prev_layer_bias
    prev_layer_params.activationIsRelu = True
    prev_layer_bn_params.gamma = gamma
    prev_layer_bn_params.beta = beta
    curr_layer_params.bias = curr_layer_bias
    curr_layer_params.weight = weight
    curr_layer_params.weightShape = weight_sz
    b_i_1, b_i = fold_high_bias_next_conv_qt(True, beta, gamma,
                                             weight.reshape(weight_sz), curr_layer_bias,
                                             prev_layer_bias)
    libpymo.updateBias(prev_layer_params, curr_layer_params, prev_layer_bn_params)
    assert (np.allclose(b_i_1, prev_layer_params.bias))
    assert (np.allclose(b_i, curr_layer_params.bias))