def METHOD_NAME(self, embeddings, labels, batch_size):
    self.curr_batch_idx = (
        torch.arange(
            self.queue_idx, self.queue_idx + batch_size, device=labels.device
        )
        % self.memory_size
    )
    self.embedding_memory[self.curr_batch_idx] = embeddings.detach()
    self.label_memory[self.curr_batch_idx] = labels.detach()
    prev_queue_idx = self.queue_idx
    self.queue_idx = (self.queue_idx + batch_size) % self.memory_size
    if (not self.has_been_filled) and (self.queue_idx <= prev_queue_idx):
        self.has_been_filled = True