def METHOD_NAME(self):
    input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
    input_mask = None
    if self.use_input_mask:
        input_mask = random_attention_mask([self.batch_size, self.seq_length])
    if input_mask is not None:
        batch_size, seq_length = input_mask.shape
        rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
        for batch_idx, start_index in enumerate(rnd_start_indices):
            input_mask[batch_idx, :start_index] = 1
            input_mask[batch_idx, start_index:] = 0
    config = self.get_config()
    return config, input_ids, input_mask