code: def __init__(
      self, discrete_bins, dim, expected_variance, store_past=0
  ):
      super().__init__()
      self.discrete_bins = discrete_bins
      self.dim = dim
      self.dist = torch.distributions.Normal(0, scale=expected_variance)
      if store_past > 0:
          self.record_past = True
          self.register_buffer(
              "accumulator_index",
              torch.zeros(1, dtype=torch.long, device="cpu"),
          )
          self.register_buffer(
              "accumulator_filled",
              torch.zeros(1, dtype=torch.long, device="cpu"),
          )
          self.register_buffer(
              "accumulator", torch.zeros(store_past, discrete_bins)
          )
      else:
          self.record_past = False
  
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 1 to create a new DiscretizationLoss object from inherited base classes: nn with example: obj = DiscretizationLoss(discrete_bins, dim, expected_variance, store_past)