code: def weighted_log_loss(yt, yp) -> Any:
      
      from keras import backend as K
  
      pos_loss = -(0 + yt) * K.log(0 + yp + K.epsilon())
      neg_loss = -(1 - yt) * K.log(1 - yp + K.epsilon())
  
      return LOSS_BIAS * K.mean(neg_loss) + (1. - LOSS_BIAS) * K.mean(pos_loss)
docs: Binary crossentropy with a bias towards false negatives
      yt: Target
      yp: Prediction