class_docstr: Tests the main functions in the library.
class_docstr_tok: ["Tests","the","main","functions","in","the","library","."]
code: def test_interval_estimation(self, method, task_bootstrap):
      def metric_func(x):
          return np.array(
              [
                  metrics.aggregate_iqm(x),
                  metrics.aggregate_mean(x),
                  metrics.aggregate_median(x),
                  metrics.aggregate_optimality_gap(x),
              ]
          )
  
      score_dict = {"method": self._x, "baseline": self._y}
      # Use small number of bootstrap samples for testing.
      point_estimates, interval_estimates = rly.get_interval_estimates(
          score_dict,
          metric_func,
          method=method,
          task_bootstrap=task_bootstrap,
          reps=100,
      )
      for key, scores in score_dict.items():
          lower_ci_estimates, upper_ci_estimates = interval_estimates[
              key
          ]
          np.testing.assert_array_equal(
              point_estimates[key], metric_func(scores)
          )
          np.testing.assert_array_less(
              lower_ci_estimates, point_estimates[key]
          )
          np.testing.assert_array_less(
              point_estimates[key], upper_ci_estimates
          )
  
file_path: rliable/tests/rliable/library_test.py
imports: from absl.testing import absltest
  from absl.testing import parameterized
  import numpy
  import rliable.library
  import rliable.metrics
label_desc: Use image node_id 4 for calling the LibraryTest obj's underlying member method code with example usage: obj.test_interval_estimation(method, task_bootstrap) and returns: np