code: def statistics(data):
      if len(data) < 2:
          std = 0
      else:
          std = stdev(data)
      total_information = (
          f"loss:{mean(data):.3f}"
          + "\u00B1"
          + f"({std/ (len(data) ** 0.5):.3f})"
      )
      recent_data = data[-32:]
      if len(recent_data) < 2:
          std = 0
      else:
          std = stdev(recent_data)
      recent_information = (
          f"recent 32 loss:{mean(recent_data):.3f}"
          + "\u00B1"
          + f"({std / (len(recent_data) ** 0.5):.3f})"
      )
      return total_information, recent_information
  
imports: import datetime
  import glob
  import html
  import os
  import inspect
  from contextlib import closing
  import modules.textual_inversion.dataset
  import torch
  import tqdm
  from einops import rearrange, repeat
  from ldm.util import default
  from modules import devices, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint, errors
  from modules.textual_inversion import textual_inversion, logging
  from modules.textual_inversion.learn_schedule import LearnRateScheduler
  from torch import einsum
  from torch.nn.init import normal_, xavier_normal_, xavier_uniform_, kaiming_normal_, kaiming_uniform_, zeros_
  from collections import deque
  from statistics import stdev, mean
label_desc: Use image node_id 9 for calling a global function with example usage: statistics(data) and returns: total_information,  recent_information