# Defining a method to generate the next character
def predict(net, char, h=None, top_k=None):
        ''' Given a character, predict the next character.
            Returns the predicted character and the hidden state.
        '''
        # tensor inputs
        x = np.array([[net.char2int[char]]])
        x = one_hot_encode(x, len(net.chars))
        inputs = torch.from_numpy(x)
        if(train_on_gpu):
            inputs = inputs.cuda()
        # detach hidden state from history
        h = tuple([each.data for each in h])
        # get the output of the model
        out, h = net(inputs, h)
        # get the character probabilities
        p = F.softmax(out, dim=1).data
        if(train_on_gpu):
            p = p.cpu() # move to cpu
        # get top characters
        if top_k is None:
            top_ch = np.arange(len(net.chars))
        else:
            p, top_ch = p.topk(top_k)
            top_ch = top_ch.numpy().squeeze()
        # select the likely next character with some element of randomness
        p = p.numpy().squeeze()
        char = np.random.choice(top_ch, p=p/p.sum())
        # return the encoded value of the predicted char and the hidden state
        return net.int2char[char], h