class ConstantTimeSampler(BaseSampler):
    def __init__(self, dataset, reweighting, model, batch_size=16,
                 backward_time=2.0, tau_th=0.9, ratio=0.5, min_a=0.2):
        self.condition = VarianceReductionCondition(tau_th)
        self.backward_time = backward_time
        self.ratio = ratio
        self.min_a = min_a
        self.model = model
        self.batch_size = batch_size
        self.N = len(dataset.train_data) if hasattr(dataset, 'train_data') else _get_dataset_length(dataset, default=1)
        print(f"Dataset size (self.N): {self.N}")
        self.usage_counts = np.zeros(self.N, dtype=int)

        super(ConstantTimeSampler, self).__init__(dataset, reweighting)

    def sample(self, batch_size):
        print(f"Entering sample method")
        print(f"batch_size: {batch_size}, self.N: {self.N}")
        print(f"Condition satisfied: {self.condition.satisfied}")
        print(f"Requested batch_size: {batch_size}")
        if batch_size > self.N:
            batch_size = min(batch_size, self.N)

        # 初始化 scores_selected
        scores_selected = None

        # 如果条件满足，进行重要性采样
        if self.condition.satisfied:
            print("Entering satisfied condition block")
            t = (1.0 + self.backward_time) * batch_size
            B = 32  # Number of samples to evaluate
            idxs = np.random.choice(self.N, B, replace=False)
            x, y = self.dataset.train_data[idxs]

            # Get sample scores
            scores = self.model.score(x, y, batch_size=B)

            # Handle possible NaN or Inf values
            scores = np.nan_to_num(scores, nan=0.0, posinf=1.0, neginf=0.0)

            # Ensure scores are positive
            scores = scores + 1e-8  # 防止出现零或负值

            # Normalize scores
            if np.max(scores) != np.min(scores):  # Avoid division by zero
                scores = (scores - np.min(scores)) / (np.max(scores) - np.min(scores))
            else:
                scores = np.full_like(scores, 0.5)

            # Scale scores to 0-7 range
            scores = scores * 7 + 1e-8  # 加上 1e-8 防止为零

            # Clip to ensure range is between 1e-8 and 7
            scores = np.clip(scores, 1e-8, 7)
            scores = np.ceil(scores)

            print("Normalized and adjusted scores:", scores)

            # Compute standard deviation of scores
            std_scores = np.std(scores)
            print("Standard deviation of scores:", std_scores)

            # Determine devide value based on std
            devide = self._determine_devide(std_scores, batch_size)
            print(f"Calculated devide value: {devide}")

            num_high = devide
            num_other = batch_size - devide

            # 如果 devide > 0，使用 GAS_for_N_max.py 选择高分样本
            if num_high > 0:
                # 此处省略具体实现
                idxs_high = np.random.choice(B, num_high, replace=False)
            else:
                idxs_high = np.array([], dtype=int)

            # 对剩余样本，使用均匀随机采样
            if num_other > 0:
                remaining_indices = np.setdiff1d(np.arange(B), idxs_high)
                if len(remaining_indices) >= num_other:
                    idxs_other = np.random.choice(remaining_indices, num_other, replace=False)
                else:
                    idxs_other = np.random.choice(remaining_indices, num_other, replace=True)
            else:
                idxs_other = np.array([], dtype=int)

            # 合并索引
            idxs2 = np.concatenate([idxs_high, idxs_other])

            if len(idxs2) == 0:
                print("Warning: Failed to get valid sample indices, falling back to random sampling.")
                return self._random_sample(batch_size)

            # 获取对应数据
            x, y = self._slice_data(x, y, idxs2)
            scores_selected = scores[idxs2]

            scores_selected = scores_selected + 1e-8  # 防止出现零值
            w = self.reweighting.sample_weights(np.arange(len(scores_selected)), scores_selected)
        else:
            # 如果条件未满足，进行均匀随机采样
            idxs = np.random.choice(self.N, batch_size, replace=False)
            idxs2 = np.arange(len(idxs))
            x, y = self.dataset.train_data[idxs]
            scores_selected = np.ones(len(idxs))
            w = np.ones((len(idxs), self.reweighting.weight_size))

        # 无论条件是否满足，都更新条件状态
        self.condition.update(scores_selected)

        self._send_messages(idxs[idxs2], (x, y), w, scores_selected)
        print(f"Returned batch shapes: x: {x.shape}, y: {y.shape}, w: {w.shape}")
        return (idxs[idxs2], (x, y), w)

    def _determine_devide(self, std_scores, batch_size):
        # 您可以根据标准差调整阈值
        # 这里简单地根据标准差大小决定 devide 值
        if std_scores > 1.0:
            devide = int(batch_size * 0.7)
        elif std_scores > 0.5:
            devide = int(batch_size * 0.5)
        else:
            devide = int(batch_size * 0.3)
        return devide

class UniformSampler(BaseSampler):
    """UniformSampler is the simplest possible sampler which samples the
    dataset uniformly."""

    def __init__(self, dataset, reweighting):
        super(UniformSampler, self).__init__(dataset, reweighting)
        # Basically if we don't know the length the indices don't matter so
        # sample batch_size 0s.
        self.idxs = np.arange(_get_dataset_length(self.dataset, default=1))

    def _get_samples_with_scores(self, batch_size):
        return (
            self.idxs,
            None,
            None
        )


class ModelSampler(BaseSampler):
    """ModelSampler uses a model to score the samples and then performs
    importance sampling based on those scores.

    It can be used to implement several training pipelines where the scoring
    model is separately trained or is sampled from the main model or is the
    main model."""

    def __init__(self, dataset, reweighting, model, large_batch=1024,
                 forward_batch_size=128):
        self.model = model
        self.large_batch = large_batch
        self.forward_batch_size = forward_batch_size
        self.N = _get_dataset_length(dataset, default=1)

        super(ModelSampler, self).__init__(dataset, reweighting)

    def _get_samples_with_scores(self, batch_size):
        assert batch_size < self.large_batch

        # Sample a large number of points in random and score them
        idxs = np.random.choice(self.N, self.large_batch)
        x, y = self.dataset.train_data[idxs]
        scores = self.model.score(x, y, batch_size=self.forward_batch_size)

        return (
            idxs,
            scores,
            (x, y)
        )


class CacheSampler(BaseSampler):
    """CacheSampler uses the recent outputs of the model to determine the
    importance of the samples"""

    def __init__(self, dataset, reweighting, staleness=3, cache_prob=0.5,
                 smooth=0.2):
        # Necessary state for implementing the cache sampler
        self._N = N = len(dataset.train_data)
        self._idxs = np.arange(N)
        self._scores = np.ones((N,))
        self._cache = {}
        self._score_sum = 0.0

        # Configuration
        self._staleness = staleness
        self._alpha = cache_prob / (1 - cache_prob)
        self._smooth = smooth

        super(CacheSampler, self).__init__(dataset, reweighting)

    def _get_samples_with_scores(self, batch_size):
        return (
            self._idxs,
            self._scores,
            None
        )

    def update(self, idxs, x):
        # Add the new scores to the cache
        for idx, xi in zip(idxs, x):
            self._cache[idx] = (self._smooth + xi, 0)

        # Remove the stale values
        self._score_sum = 0.0
        keys = self._cache.keys()
        for k in keys:
            item = self._cache[k]
            if item[1] > self._staleness:
                self._scores[k] = 1
                del self._cache[k]
            else:
                self._cache[k] = (item[0], item[1] + 1)
                self._score_sum += item[0]

        # Recompute the scores for sampling
        N = self._N
        S = self._score_sum
        a = self._alpha
        for k, (s, _) in self._cache.items():
            self._scores[k] = 1 + a * N * s / S
仔细阅读上述代码，告诉我为什么devide的值在运行之后一直是11，这是为什么，详细进行分析给出理由