class_docstr: The running mode of this script. Available values are:
  - extract: Only import the model and extract tuning tasks from it.
  - tune: Only tune the tasks and create the tuning database.
  - eval: Only benchmark model using pre-existing tuning database.
  - all: Run both tuning and benchmark
docstr: Returns whether it should tune the tasks.
docstr_tok: ["Returns","whether","it","should","tune","the","tasks","."]
code: def should_tune(self):
      """
      Returns whether it should tune the tasks.
      """
      return self in (RunMode.ALL, RunMode.TUNE)
  
imports: import argparse
  import contextlib
  import logging
  import os
  import pickle
  import sys
  import warnings
  from collections import defaultdict
  from enum import Enum
  from typing import Callable, Dict, List, Tuple
  import numpy
  import torch
  from scipy.stats import ttest_ind
  import tvm
  import tvm.relay
  from tvm import meta_schedule
  from tvm._ffi import get_global_func
  from tvm.contrib.graph_executor import GraphModule
  from tvm.meta_schedule.testing.torchbench.utils import DisallowedOperator, load_torchdynamo_benchmark_runner, same, timed
  from tvm.runtime.vm import VirtualMachine
  from tvm.support import describe
  import torchdynamo
label_desc: Use image node_id 2 for calling the RunMode obj's underlying member method code with example usage: obj.should_tune() and returns: unknown