class_docstr: Estimate probability density at required points given an RDD of samples
  from the population.
  
  Examples
  --------
  >>> kd = KernelDensity()
  >>> sample = sc.parallelize([0.0, 1.0])
  >>> kd.setSample(sample)
  >>> kd.estimate([0.0, 1.0])
  array([ 0.12938758,  0.12938758])
docstr: Estimate the probability density at points
code: def estimate(self, points: Iterable[float]) -> ndarray:
      """Estimate the probability density at points"""
      points = list(points)
      densities = callMLlibFunc(
          "estimateKernelDensity", self._sample, self._bandwidth, points
      )
      return np.asarray(densities)
  
file_path: spark/python/pyspark/mllib/stat/KernelDensity.py
imports: from typing import Iterable, Optional
  import numpy
  from numpy import ndarray
  from pyspark.mllib.common import callMLlibFunc
  from pyspark.rdd import RDD
label_desc: Use image node_id 4 for calling the KernelDensity obj's underlying member method code with example usage: obj.estimate(points) and returns: np