docstr: Runs test
  
  Parameters
  ----------
  predict_and_confidence_fn : function
      Takes as input a list of examples
      Outputs a tuple (predictions, confidences)
  overwrite : bool
      If False, raise exception if results already exist
  verbose : bool
      If True, print extra information
  n : int
      If not None, number of samples to draw
  seed : int
      Seed to use if n is not None
docstr_tok: ["Runs","test","Parameters","--","--","--","--","--","predict_and_confidence_fn",":","function","Takes","as","input","a","list","of","examples","Outputs","a","tuple","(","predictions",",","confidences",")","overwrite",":","bool","If","False",",","raise","exception","if","results","already","exist","verbose",":","bool","If","True",",","print","extra","information","n",":","int","If","not","None",",","number","of","samples","to","draw","seed",":","int","Seed","to","use","if","n","is","not","None"]
code: def run(
      self,
      predict_and_confidence_fn,
      overwrite=False,
      verbose=True,
      n=None,
      seed=None,
  ):
      """Runs test
  
      Parameters
      ----------
      predict_and_confidence_fn : function
          Takes as input a list of examples
          Outputs a tuple (predictions, confidences)
      overwrite : bool
          If False, raise exception if results already exist
      verbose : bool
          If True, print extra information
      n : int
          If not None, number of samples to draw
      seed : int
          Seed to use if n is not None
  
      """
      # Checking just to avoid predicting in vain, will be created in run_from_preds_confs
      self._check_create_results(overwrite, check_only=True)
      examples, result_indexes = self.example_list_and_indices(
          n, seed=seed
      )
  
      if verbose:
          print("Predicting %d examples" % len(examples))
      preds, confs = predict_and_confidence_fn(examples)
      self.run_from_preds_confs(preds, confs, overwrite=overwrite)
  
imports: from abc import ABC, abstractmethod
  import dill
  from munch import Munch
  import numpy
  import inspect
  from .expect import iter_with_optional, Expect
  from .viewer.test_summarizer import TestSummarizer
label_desc: Use image node_id 18 for calling the AbstractTest obj's underlying member method code with example usage: obj.run(predict_and_confidence_fn, overwrite, verbose, n, seed) without return types