code: def __init__(
      self, diffusion, history_per_term=10, uniform_prob=0.001
  ):
      self.diffusion = diffusion
      self.history_per_term = history_per_term
      self.uniform_prob = uniform_prob
      self._loss_history = np.zeros(
          [diffusion.num_timesteps, history_per_term], dtype=np.float64
      )
      self._loss_counts = np.zeros(
          [diffusion.num_timesteps], dtype=np.int
      )
  
imports: from abc import ABC, abstractmethod
  import numpy
  import torch
  from scipy.stats import norm
  import torch.distributed
label_desc: Use image node_id 1 to create a new LossSecondMomentResampler object from inherited base classes: LossAwareSampler with example: obj = LossSecondMomentResampler(diffusion, history_per_term, uniform_prob)