code: def compile_results(infer, memo, df, of, op, alpha, size):
      data = []
      try:
          idx = memo.find("init=")
          max_size = float(
              memo[idx + 5 : idx + memo[idx:].find(" ") - 1]
          )
      except:
          max_size = float("nan")
      idx = memo.find("overall=")
      try:
          malloc_size = float(memo[idx + 8 :])
      except:
          malloc_size = float("nan")
      idx = infer.find("Warmup (avg):")
      try:
          warmup = float(infer[idx + 14 : idx + infer[idx:].find(",")])
      except:
          warmup = float("nan")
      idx = infer.find("Init:")
      try:
          init = float(infer[idx + 6 : idx + infer[idx:].find(",")])
      except:
          init = float("nan")
      idx = infer.find("Inference (avg):")
      try:
          no_stat = float(infer[idx + 17 :])
      except:
          no_stat = float("nan")
      df = df.append(
          pd.Series(
              [
                  of,
                  alpha,
                  size,
                  op,
                  max_size,
                  malloc_size,
                  warmup,
                  init,
                  no_stat,
              ],
              index=df.columns,
          ),
          ignore_index=True,
      )
      return df
  
file_path: auptimizer/Examples/converter_examples/Convert_Benchmark/script_mobilenet.py
imports: import keras
  import subprocess
  from os import path
  import pandas
  import json
label_desc: Use image node_id 4 for calling a global function with example usage: compile_results(infer, memo, df, of, op, alpha, size) and returns: df