class_docstr: Instance balanced sampler that samples equal number of positive samples
  for each instance.
class_docstr_tok: ["Instance","balanced","sampler","that","samples","equal","number","of","positive","samples","for","each","instance","."]
docstr: Sample positive boxes.
  
  Args:
      assign_result (:obj:`AssignResult`): The assigned results of boxes.
      num_expected (int): The number of expected positive samples
  
  Returns:
      Tensor or ndarray: sampled indices.
docstr_tok: ["Sample","positive","boxes",".","Args",":","assign_result","(",":","obj",":","`","AssignResult","`",")",":","The","assigned","results","of","boxes",".","num_expected","(","int",")",":","The","number","of","expected","positive","samples","Returns",":","Tensor","or","ndarray",":","sampled","indices","."]
code: def _sample_pos(self, assign_result, num_expected, **kwargs):
      """Sample positive boxes.
  
      Args:
          assign_result (:obj:`AssignResult`): The assigned results of boxes.
          num_expected (int): The number of expected positive samples
  
      Returns:
          Tensor or ndarray: sampled indices.
      """
      pos_inds = torch.nonzero(
          assign_result.gt_inds > 0, as_tuple=False
      )
      if pos_inds.numel() != 0:
          pos_inds = pos_inds.squeeze(1)
      if pos_inds.numel() <= num_expected:
          return pos_inds
      else:
          unique_gt_inds = assign_result.gt_inds[pos_inds].unique()
          num_gts = len(unique_gt_inds)
          num_per_gt = int(round(num_expected / float(num_gts)) + 1)
          sampled_inds = []
          for i in unique_gt_inds:
              inds = torch.nonzero(
                  assign_result.gt_inds == i.item(), as_tuple=False
              )
              if inds.numel() != 0:
                  inds = inds.squeeze(1)
              else:
                  continue
              if len(inds) > num_per_gt:
                  inds = self.random_choice(inds, num_per_gt)
              sampled_inds.append(inds)
          sampled_inds = torch.cat(sampled_inds)
          if len(sampled_inds) < num_expected:
              num_extra = num_expected - len(sampled_inds)
              extra_inds = np.array(
                  list(set(pos_inds.cpu()) - set(sampled_inds.cpu()))
              )
              if len(extra_inds) > num_extra:
                  extra_inds = self.random_choice(extra_inds, num_extra)
              extra_inds = (
                  torch.from_numpy(extra_inds)
                  .to(assign_result.gt_inds.device)
                  .long()
              )
              sampled_inds = torch.cat([sampled_inds, extra_inds])
          elif len(sampled_inds) > num_expected:
              sampled_inds = self.random_choice(
                  sampled_inds, num_expected
              )
          return sampled_inds
  
imports: import numpy
  import torch
  from mmdet.registry import TASK_UTILS
  from .random_sampler import RandomSampler
label_desc: Use image node_id 1 for calling the InstanceBalancedPosSampler obj's underlying member method code with example usage: obj._sample_pos(assign_result, num_expected) and returns: pos_inds, sampled_inds