code: def find_hard_negatives(
      dmat, output_index=True, empirical_thresh=0.0
  ):
      """
      a = A * P'
      A: N * ndim
      P: N * ndim
  
      a1p1 a1p2 a1p3 a1p4 ...
      a2p1 a2p2 a2p3 a2p4 ...
      a3p1 a3p2 a3p3 a3p4 ...
      a4p1 a4p2 a4p3 a4p4 ...
      ...  ...  ...  ...
      """
  
      r, c = dmat.size()  # Correct bug
  
      if not output_index:
          pos = torch.zeros(max(r, c))  # Correct bug
          pos[: min(r, c)] = dmat.diag()  # Correct bug
  
      dmat = (
          dmat + torch.eye(r, c).to(dmat.device) * 99999
      )  # filter diagonal     # Correct bug
      dmat[
          dmat < empirical_thresh
      ] = 99999  # filter outliers in brown dataset
  
      # Add following 3 lines to solve a bug
      min_a, min_p = torch.zeros(max(r, c)), torch.zeros(
          max(r, c)
      )  # Check for unequal number of anchors and positives
      min_a[:c], _ = torch.min(dmat, dim=0)
      min_p[:r], _ = torch.min(dmat, dim=1)
  
      if not output_index:
          neg = torch.min(min_a, min_p)
          return pos, neg.to(dtype=pos.dtype)
  
imports: import torch
  import torch.nn
  from pytorch_metric_learning.utils import common_functions
  from pytorch_metric_learning.utils import loss_and_miner_utils
  import unittest
  from pytorch_metric_learning.losses import DynamicSoftMarginLoss
  from ..None import TEST_DEVICE, TEST_DTYPES
  from ..zzz_testing_utils.testing_utils import angle_to_coord
label_desc: Use image node_id 2 for calling a global function with example usage: find_hard_negatives(dmat, output_index, empirical_thresh) and returns: pos,  neg