code: def _evaluate_one_batch_member(
      to_reduce, expected, reduction_size, tolerance
  ):
      """As `_evaluate_means_assertion` but for one batch member."""
      lb = expected - tolerance
      ub = expected + tolerance
  
      # This is our budget of draws from np.random.choice.  Chosen to
      # avoid the bootstraps being much more expensive than the code under
      # test.
      fuel = 30000000
      bootstrapped_means = _bootstrap_means(
          to_reduce, reduction_size, fuel
      )
      bootstrap_result = _evaluate_bootstrap(bootstrapped_means, lb, ub)
      loc, scale, k_s_limit = _fit_gaussian(to_reduce, reduction_size)
      gauss_result = _evaluate_assuming_gaussian(
          loc, scale, k_s_limit, expected, lb, ub
      )
      return bootstrap_result, gauss_result
  
imports: import collections
  import numpy
  import scipy.optimize
  import scipy.stats
label_desc: Use image node_id 7 for calling a global function with example usage: _evaluate_one_batch_member(to_reduce, expected, reduction_size, tolerance) and returns: bootstrap_result,  gauss_result