code: def __init__(self, L=15, norm=False):
      self.values = None
      self.L = L
      self.norm = norm
  
imports: import logging
  import math
  import os
  from typing import List, Optional, Union
  from collections import defaultdict
  from pathlib import Path
  import numpy
  from PIL import Image
  import cv2
  import timm
  import torch
  import torch.nn
  import torch.nn.functional
  from PIL import ImageOps
  from timm.models.swin_transformer import SwinTransformer
  from torchvision.transforms.functional import resize, rotate
  from transformers import PreTrainedTokenizerFast, StoppingCriteria, StoppingCriteriaList, MBartConfig, MBartForCausalLM
  from transformers.file_utils import ModelOutput
  from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
  from nougat.postprocessing import postprocess
  from nougat.transforms import train_transform, test_transform
label_desc: Use image node_id 1 to create a new RunningVarTorch object  with example: obj = RunningVarTorch(L, norm)