text: import torch
  
  from .base_sampler import BaseSampler
  from .sampling_result import SamplingResult
  
  
  class PseudoSampler(BaseSampler):
  
      def __init__(self, **kwargs):
          pass
  
      def _sample_pos(self, **kwargs):
          raise NotImplementedError
  
      def _sample_neg(self, **kwargs):
          raise NotImplementedError
  
      def sample(self, assign_result, bboxes, gt_bboxes, gt_keypoints=None, **kwargs):
          pos_inds = torch.nonzero(
              assign_result.gt_inds > 0).squeeze(-1).unique()
          neg_inds = torch.nonzero(
              assign_result.gt_inds == 0).squeeze(-1).unique()
          gt_flags = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8)
          sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
                                           assign_result, gt_flags, gt_keypoints=gt_keypoints)
          return sampling_result
  