code: def _format_bootstrap_report(result):
      """Formats a `_BootstrapResult` as a complete report str.
  
      The information presented includes:
      - The results of a bootstrap to test how often the test fails
      - A confidence interval on the probability of the
        `assertAllMeansClose` failing, assuming the empirical distribution
        is close to the true distribution
  
      This differs conceptually from the `_gaussian_report` because it
      doesn't assume that the distribution on means Gaussianizes, so
      remains valid in a low-sample setting.  On the other hand, when
      trustworthy, `_gaussian_report` is more informative.
  
      Args:
        result: A `_BootstrapResult` capturing the fit.
  
      Returns:
        report: A Python  `str` suitable for being printed or added to an
          assertion message.
  
      """
      report = (
          f"\n{result.failures} of {result.trials} bootstrapped trials fail."
          "\nAssuming that the empirical distribution is the true distribution:"
      )
      for rate in [1e-3, 1e-9]:
          one_rate = result.for_error_rate(rate)
          report += (
              f"\n- With confidence 1 - {rate}, p(fail) >= {one_rate.low_p:.3g}."
              f"\n- With confidence 1 - {rate}, p(fail) <= {one_rate.high_p:.3g}."
          )
      return report
  
imports: import collections
  import numpy
  import scipy.optimize
  import scipy.stats
label_desc: Use image node_id 9 for calling a global function with example usage: _format_bootstrap_report(result) and returns: report