How to use the d2l.data.data_iter_random function in d2l

def train_and_predict_rnn(rnn, get_params, init_rnn_state, num_hiddens,
                          corpus_indices, vocab, device, is_random_iter,
                          num_epochs, num_steps, lr, clipping_theta,
                          batch_size, prefixes):
    """Train an RNN model and predict the next item in the sequence."""
    if is_random_iter:
        data_iter_fn = data_iter_random
    else:
        data_iter_fn = data_iter_consecutive
    params = get_params()
    loss =  nn.CrossEntropyLoss()
    start = time.time()
    for epoch in range(num_epochs):
        if not is_random_iter:
            # If adjacent sampling is used, the hidden state is initialized
            # at the beginning of the epoch
            state = init_rnn_state(batch_size, num_hiddens, device)
        l_sum, n = 0.0, 0
        data_iter = data_iter_fn(corpus_indices, batch_size, num_steps, device)
        for X, Y in data_iter:
            if is_random_iter:
                # If random sampling is used, the hidden state is initialized
                # before each mini-batch update