code: def forward(self, x):
      other_dims = set(range(len(x.shape))) - set([self.dim])
      averaged = x.sum(dim=tuple(other_dims)) / x.sum()
      averaged = averaged - averaged.mean()
  
      if self.record_past:
          acc_count = self.accumulator.shape[0]
          avg = averaged.detach().clone()
          if self.accumulator_filled > 0:
              averaged = (
                  torch.mean(self.accumulator, dim=0)
                  * (acc_count - 1)
                  / acc_count
                  + averaged / acc_count
              )
  
          # Also push averaged into the accumulator.
          self.accumulator[self.accumulator_index] = avg
          self.accumulator_index += 1
          if self.accumulator_index >= acc_count:
              self.accumulator_index *= 0
              if self.accumulator_filled <= 0:
                  self.accumulator_filled += 1
  
      return torch.sum(-self.dist.log_prob(averaged))
  
imports: import functools
  from math import sqrt
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from einops import rearrange
label_desc: Use image node_id 2 for calling the DiscretizationLoss obj's underlying member method code with example usage: obj.forward(x) and returns: torch