def _static_subsample(self, indicator, batch_size, labels):
    """Returns subsampled minibatch.

    Args:
      indicator: boolean tensor of shape [N] whose True entries can be sampled.
        N should be a complie time constant.
      batch_size: desired batch size. This scalar cannot be None.
      labels: boolean tensor of shape [N] denoting positive(=True) and negative
        (=False) examples. N should be a complie time constant.

    Returns:
      sampled_idx_indicator: boolean tensor of shape [N], True for entries which
        are sampled. It ensures the length of output of the subsample is always
        batch_size, even when number of examples set to True in indicator is
        less than batch_size.

    Raises:
      ValueError: if labels and indicator are not 1D boolean tensors.
    """
    # Check if indicator and labels have a static size.
    if not indicator.shape.is_fully_defined():
      raise ValueError('indicator must be static in shape when is_static is'
                       'True')
    if not labels.shape.is_fully_defined():
      raise ValueError('labels must be static in shape when is_static is'
                       'True')
    if not isinstance(batch_size, int):
      raise ValueError('batch_size has to be an integer when is_static is'
                       'True.')

    input_length = tf.shape(indicator)[0]

    # Set the number of examples set True in indicator to be at least
    # batch_size.
    num_true_sampled = tf.reduce_sum(tf.cast(indicator, tf.float32))
    additional_false_sample = tf.less_equal(
        tf.cumsum(tf.cast(tf.logical_not(indicator), tf.float32)),
        batch_size - num_true_sampled)
    indicator = tf.logical_or(indicator, additional_false_sample)

    # Shuffle indicator and label. Need to store the permutation to restore the
    # order post sampling.
    permutation = tf.random_shuffle(tf.range(input_length))
    indicator = tf.gather(indicator, permutation, axis=0)
    labels = tf.gather(labels, permutation, axis=0)

    # index (starting from 1) when indicator is True, 0 when False
    indicator_idx = tf.where(
        indicator, tf.range(1, input_length + 1),
        tf.zeros(input_length, tf.int32))

    # Replace -1 for negative, +1 for positive labels
    signed_label = tf.where(
        labels, tf.ones(input_length, tf.int32),
        tf.scalar_mul(-1, tf.ones(input_length, tf.int32)))
    # negative of index for negative label, positive index for positive label,
    # 0 when indicator is False.
    signed_indicator_idx = tf.multiply(indicator_idx, signed_label)
    sorted_signed_indicator_idx = tf.nn.top_k(
        signed_indicator_idx, input_length, sorted=True).values

    [num_positive_samples,
     num_negative_samples] = self._get_num_pos_neg_samples(
         sorted_signed_indicator_idx, batch_size)

    sampled_idx = self._get_values_from_start_and_end(
        sorted_signed_indicator_idx, num_positive_samples,
        num_negative_samples, batch_size)

    # Shift the indices to start from 0 and remove any samples that are set as
    # False.
    sampled_idx = tf.abs(sampled_idx) - tf.ones(batch_size, tf.int32)
    sampled_idx = tf.multiply(
        tf.cast(tf.greater_equal(sampled_idx, tf.constant(0)), tf.int32),
        sampled_idx)

    sampled_idx_indicator = tf.cast(tf.reduce_sum(
        tf.one_hot(sampled_idx, depth=input_length),
        axis=0), tf.bool)

    # project back the order based on stored permutations
    idx_indicator = tf.scatter_nd(
        tf.expand_dims(permutation, -1), sampled_idx_indicator,
        shape=(input_length,))
    return idx_indicator
Returns subsampled minibatch.

    Args:
      indicator: boolean tensor of shape [N] whose True entries can be sampled.
        N should be a complie time constant.
      batch_size: desired batch size. This scalar cannot be None.
      labels: boolean tensor of shape [N] denoting positive(=True) and negative
        (=False) examples. N should be a complie time constant.

    Returns:
      sampled_idx_indicator: boolean tensor of shape [N], True for entries which
        are sampled. It ensures the length of output of the subsample is always
        batch_size, even when number of examples set to True in indicator is
        less than batch_size.

    Raises:
      ValueError: if labels and indicator are not 1D boolean tensors.
    
inference_api/src/main/object_detection/core/balanced_positive_negative_sampler.py