code: def parse_file_name(model_path):
      """
      Customize this to work for your models
      """
      fields = Path(model_path).name.split("_")
      training, architecture = fields[:2]
      if training == "supervised":
          training = "sup"
      if "regnet" in architecture:
          if "256" in architecture:
              architecture = "rg256"
          elif "128" in architecture:
              architecture = "rg128"
          elif "16" in architecture:
              architecture = "rg16"
      for i in range(2, len(fields)):
          if fields[i].startswith("in"):
              tdata, tdata_source, tdata_size = fields[i], "in", "1m"
              break
          elif fields[i].startswith("ig"):
              tdata, tdata_source, tdata_size = (
                  fields[i],
                  "ig",
                  fields[i][2:],
              )
              if not tdata_size:
                  tdata_size = "10b"
              break
      else:
          raise ValueError(
              f"data source and size not found for {model_path}"
          )
      return training, architecture, tdata, tdata_source, tdata_size
  
imports: import json
  import os
  from math import ceil, floor
  from pathlib import Path
  import matplotlib.pyplot
  import numpy
  import pandas
  import seaborn
  from IPython.display import display_html
  from matplotlib.ticker import FixedLocator
  from vissl.utils.io import load_file, save_file
label_desc: Use image node_id 6 for calling a global function with example usage: parse_file_name(model_path) and returns: training,  architecture,  tdata,  tdata_source,  tdata_size