class_docstr: Univariate and bivariate kernel density estimator.
class_docstr_tok: ["Univariate","and","bivariate","kernel","density","estimator","."]
docstr: Fit and evaluate on univariate or bivariate data.
docstr_tok: ["Fit","and","evaluate","on","univariate","or","bivariate","data","."]
code: def __call__(self, x1, x2=None, weights=None):
      """Fit and evaluate on univariate or bivariate data."""
      if x2 is None:
          return self._eval_univariate(x1, weights)
      else:
          return self._eval_bivariate(x1, x2, weights)
  
imports: from numbers import Number
  from statistics import NormalDist
  import numpy
  import pandas
  from .algorithms import bootstrap
  from .utils import _check_argument
label_desc: Use image node_id 9 for calling the KDE obj's underlying member method code with example usage: obj.__call__(x1, x2, weights) and returns: self, self