text: import numpy as np
  import torch
  from .strategy import Strategy
  
  class LeastConfidenceDropout(Strategy):
  	def __init__(self, X, Y, idxs_lb, net, handler, args, n_drop=10):
  		super(LeastConfidenceDropout, self).__init__(X, Y, idxs_lb, net, handler, args)
  		self.n_drop = n_drop
  
  	def query(self, n):
  		idxs_unlabeled = np.arange(self.n_pool)[~self.idxs_lb]
  		probs = self.predict_prob_dropout(self.X[idxs_unlabeled], self.Y[idxs_unlabeled], self.n_drop)
  		U = probs.max(1)[0]
  		return idxs_unlabeled[U.sort()[1][:n]]
  