class_docstr: bla
class_docstr_tok: ["bla"]
code: def explain_instance(
      self,
      text,
      classifier_fn,
      threshold=0.95,
      delta=0.1,
      tau=0.15,
      batch_size=10,
      onepass=False,
      use_proba=False,
      beam_size=4,
      **kwargs,
  ):
      if type(text) == bytes:
          text = text.decode()
      words, positions, true_label, sample_fn = self.get_sample_fn(
          text, classifier_fn, onepass=onepass, use_proba=use_proba
      )
      # print words, true_label
      exp = anchor_base.AnchorBaseBeam.anchor_beam(
          sample_fn,
          delta=delta,
          epsilon=tau,
          batch_size=batch_size,
          desired_confidence=threshold,
          stop_on_first=True,
          coverage_samples=1,
          **kwargs,
      )
      exp["names"] = [words[x] for x in exp["feature"]]
      exp["positions"] = [positions[x] for x in exp["feature"]]
      exp["instance"] = text
      exp["prediction"] = true_label
      explanation = anchor_explanation.AnchorExplanation(
          "text", exp, self.as_html
      )
      return explanation
  
imports: from .None import utils
  from .None import anchor_base
  from .None import anchor_explanation
  import numpy
  import json
  import os
  import string
  import sys
  from io import open
  import numpy
label_desc: Use image node_id 3 for calling the AnchorText obj's underlying member method code with example usage: obj.explain_instance(text, classifier_fn, threshold, delta, tau, batch_size, onepass, use_proba, beam_size) and returns: explanation