def RNN_forward_prop_step(parameters, a_prev, x):
    W_aa = parameters['W_aa']
    W_ax = parameters['W_ax']
    W_ya = parameters['W_ya']
    b_y = parameters['b_y']
    b = parameters['b']

    # Compute hidden state
    a_next = np.tanh(np.dot(W_ax, x) + np.dot(W_aa, a_prev) + b)

    # Compute log probabilities for next character
    p_t = softmax(np.dot(W_ya, a_next) + b_y)

    return a_next, p_t