class_docstr: BenchmarkRunner is responsible for benchmarking all the registered
  benchmark test groups.
  
  Attributes:
      tag_filter (str): control the benchmarks which matches the tag.
      operator (str): only run benchmark test cases that contains
  this filter string in the test case's id.
      test_name (str): only run benchmark test cases that matches this filter,
      this is a case-sensitive substring match and it happens in
      the _keep_test method.
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docstr: This function decides whether the measured time can be reported based on the
  following conditions: 1) the number of iterations is larger than the max_iters.
  2) the execution time is larger than the predefined minimum_time
  3) the execution time is larger than user defined minimum_time
docstr_tok: ["This","function","decides","whether","the","measured","time","can","be","reported","based","on","the","following","conditions",":","1",")","the","number","of","iterations","is","larger","than","the","max_iters",".","2",")","the","execution","time","is","larger","than","the","predefined","minimum_time","3",")","the","execution","time","is","larger","than","user","defined","minimum_time"]
code: def _iteration_result_is_significant(
      self,
      iters,
      run_time_sec,
      curr_test_total_time,
      has_explicit_iteration_count,
  ):
      """This function decides whether the measured time can be reported based on the
      following conditions: 1) the number of iterations is larger than the max_iters.
      2) the execution time is larger than the predefined minimum_time
      3) the execution time is larger than user defined minimum_time
      """
      return (
          iters > self.max_iters
          or run_time_sec > self.predefined_minimum_secs
          or has_explicit_iteration_count
      ) and curr_test_total_time > self.args.min_time_per_test
  
imports: import ast
  import copy
  import functools
  import json
  import timeit
  from collections import namedtuple
  import benchmark_utils
  import numpy
  import torch
  import torch.utils.cpp_extension
label_desc: Use image node_id 5 for calling the BenchmarkRunner obj's underlying member method code with example usage: obj._iteration_result_is_significant(iters, run_time_sec, curr_test_total_time, has_explicit_iteration_count) and returns: unknown