code: def test_cache(self):
      # warning: this could be a flakey test
      # for N times, run the function twice and compare the time of each run.
      # the second run should be faster since the output is cached
      # to make sure the time is not too small, we run this on an input dict that is
      # arbitrarily large and nested.
      # we also check if cache is not working the second run is as slow as the first
      # run.
  
      input_dict = NestedDict({"input": 2})
      for i in range(100):
          inds = (str(i),) + tuple(str(j) for j in range(i + 1, i + 11))
          input_dict[inds] = i
  
      N = 500
      time1, time2 = [], []
      for _ in range(N):
          module = CorrectImplementation()
  
          fn = getattr(module, "check_input_and_output_with_cache")
          start = time.time()
          fn(input_dict)
          end = time.time()
          time1.append(end - start)
  
          start = time.time()
          fn(input_dict)
          end = time.time()
          time2.append(end - start)
  
      lower_bound_time1 = np.mean(time1)  # - 3 * np.std(time1)
      upper_bound_time2 = np.mean(time2)  # + 3 * np.std(time2)
      print(f"time1: {np.mean(time1)}")
      print(f"time2: {np.mean(time2)}")
  
      self.assertGreater(lower_bound_time1, upper_bound_time2)
  
imports: import abc
  import time
  import unittest
  from typing import Dict, Any, Type
  import numpy
  import torch
  from ray.rllib.core.models.specs.checker import SpecCheckingError
  from ray.rllib.core.models.specs.checker import convert_to_canonical_format, check_input_specs, check_output_specs
  from ray.rllib.core.models.specs.specs_base import TensorSpec, TypeSpec
  from ray.rllib.core.models.specs.specs_dict import SpecDict
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.nested_dict import NestedDict
label_desc: Use image node_id 6 for calling the TestCheckSpecs obj's underlying member method code with example usage: obj.test_cache() without return types