code: def brief_report(to_reduce, expected, axis, atol, rtol):
      """Evaluates `assertAllMeansClose` assertion quality and reports briefly.
  
      Specifically, uses a Gaussian approximation to estimate
      - The probability of the assertion failing
      - How to change the parameters to control the failure probability
  
      The evaluation assumes that the elements of `to_reduce` are IID
      along the given `axis`, and that the empirical distribution they
      represent is a good approximation of the true one-element
      distribution.  The analysis also assumes that the reduction axis is
      large enough that the distribution on the computed mean is well
      approximated as a Gaussian.
  
      The brief report only describes the most likely to fail batch
      member.  This introduces some statistical skew: the samples being
      bootstrapped from are not for the true distribution of the batch
      member under test, but from the distribution conditioned on that
      batch member having turned to be the worst in the batch.  This may
      grow problematic for a large batch of `assertAllMeansClose`, where
      multiple members have a non-negligible true probability of failure.
      If that source of error is not worth the brevity, there's always
      `full_report`.
  
      Args:
        to_reduce: Numpy array of samples, presumed IID along `axis`.
          Other dimensions are taken to be batch dimensions.
        expected: Numpy array of expected mean values.  Must broadcast
          with the reduction of `to_reduce` along `axis`.
        axis: Python `int` giving the reduction axis.
        atol: Python `float`, absolute tolerance for the means.
        rtol: Python `float`, relative tolerance for the means.
  
      Returns:
        report: A Python `str` suitable for being printed or added to an
          assertion message.
      """
      result = _evaluate_means_assertion(
          to_reduce, expected, axis, atol, rtol
      )
      return _format_brief_report(result)
  
imports: import collections
  import numpy
  import scipy.optimize
  import scipy.stats
label_desc: Use image node_id 14 for calling a global function with example usage: brief_report(to_reduce, expected, axis, atol, rtol) and returns: _format_brief_report