class_docstr: Univariate and bivariate kernel density estimator.
class_docstr_tok: ["Univariate","and","bivariate","kernel","density","estimator","."]
docstr: Create a 1D grid of evaluation points.
docstr_tok: ["Create","a","1D","grid","of","evaluation","points","."]
code: def _define_support_univariate(self, x, weights):
      """Create a 1D grid of evaluation points."""
      kde = self._fit(x, weights)
      bw = np.sqrt(kde.covariance.squeeze())
      grid = self._define_support_grid(
          x, bw, self.cut, self.clip, self.gridsize
      )
      return grid
  
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 3 for calling the KDE obj's underlying member method code with example usage: obj._define_support_univariate(x, weights) and returns: grid