class_docstr: Explains predictions on tabular (i.e. matrix) data.
  For numerical features, perturb them by sampling from a Normal(0,1) and
  doing the inverse operation of mean-centering and scaling, according to the
  means and stds in the training data. For categorical features, perturb by
  sampling according to the training distribution, and making a binary
  feature that is 1 when the value is the same as the instance being
  explained.
class_docstr_tok: ["Explains","predictions","on","tabular","(","i.e",".","matrix",")","data",".","For","numerical","features",",","perturb","them","by","sampling","from","a","Normal","(","0,1",")","and","doing","the","inverse","operation","of","mean-centering","and","scaling",",","according","to","the","means","and","stds","in","the","training","data",".","For","categorical","features",",","perturb","by","sampling","according","to","the","training","distribution",",","and","making","a","binary","feature","that","is","1","when","the","value","is","the","same","as","the","instance","being","explained","."]
code: def convert_and_round(values):
      return ["%.2f" % v for v in values]
  
imports: import collections
  import copy
  from functools import partial
  import json
  import warnings
  import numpy
  import scipy
  import sklearn
  import sklearn.preprocessing
  from sklearn.utils import check_random_state
  from pyDOE2 import lhs
  from scipy.stats.distributions import norm
  from lime.discretize import QuartileDiscretizer
  from lime.discretize import DecileDiscretizer
  from lime.discretize import EntropyDiscretizer
  from lime.discretize import BaseDiscretizer
  from lime.discretize import StatsDiscretizer
  from .None import explanation
  from .None import lime_base
label_desc: Use image node_id 2 for calling the LimeTabularExplainer obj's underlying member method code with example usage: obj.convert_and_round(values) and returns: unknown