def sample(parameters, chars_to_encoding):
    W_aa = parameters['W_aa']
    W_ax = parameters['W_ax']
    W_ya = parameters['W_ya']
    b_y = parameters['b_y']
    b = parameters['b']
    vocab_size = b_y.shape[0]
    n_a = W_aa.shape[1]

    x = np.zeros((vocab_size,))
    a_prev = np.zeros((n_a,))

    indices = []
    idx = -1 

    counter = 0
    newline_character = chars_to_encoding['n']

    while (idx != newline_character and counter != 50):
        a = np.tanh(np.dot(W_ax,x)+np.dot(W_aa,a_prev)+np.ravel(b))
        z = np.dot(W_ya,a) + np.ravel(b_y)
        y = softmax(z)

        idx = np.random.choice(list(chars_to_encoding.values()), p=np.ravel(y))
        indices.append(idx)    

        x = np.zeros((vocab_size,))
        x[idx] = 1

        a_prev = a

        counter +=1
        if (counter == 50):
            indices.append(chars_to_encoding['n'])
    return indices