code: def debiased_mean_var(self):
      debiased_mean = self.mean / self.debiasing_term.clamp(
          min=self.epsilon
      )
      debiased_mean_sq = self.mean_sq / self.debiasing_term.clamp(
          min=self.epsilon
      )
      debiased_var = (debiased_mean_sq - debiased_mean**2).clamp(
          min=1e-2
      )
      return debiased_mean, debiased_var
  
imports: import torch
  import torch.nn
  from torch.distributions import Categorical
  import numpy
  import parl
label_desc: Use image node_id 4 for calling the PopArt obj's underlying member method code with example usage: obj.debiased_mean_var() and returns: debiased_mean,  debiased_var