code: def weights(self):
      if not self._warmed_up():
          return np.ones(
              [self.diffusion.num_timesteps], dtype=np.float64
          )
      weights = np.sqrt(np.mean(self._loss_history**2, axis=-1))
      weights /= np.sum(weights)
      weights *= 1 - self.uniform_prob
      weights += self.uniform_prob / len(weights)
      return weights
  
imports: from abc import ABC, abstractmethod
  import numpy
  import torch
  from scipy.stats import norm
  import torch.distributed
label_desc: Use image node_id 2 for calling the LossSecondMomentResampler obj's underlying member method code with example usage: obj.weights() and returns: weights, np