class_docstr: General-purpose sampler for dense continuous data, based on multivariate
  kernel density estimation.
  
  The limitation is that a single bandwidth value is used for all dimensions,
  i.e. bandwith matrix is a positive scalar times the identity matrix.
  It is a problem e.g. when features have different variances
  (e.g. some of them are one-hot encoded and other are continuous).
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code: def sample_near(self, doc, n_samples=1):
      # XXX: it doesn't sample only near the given document, it
      # samples everywhere
      doc = np.asarray(doc)
      samples = self.kde_.sample(n_samples, random_state=self.rng_)
      return samples, self._similarity(doc, samples)
  
imports: from __future__ import absolute_import
  import abc
  from functools import partial
  from typing import List, Tuple, Any, Union, Dict, Optional
  import six
  import numpy
  from scipy.stats import itemfreq
  from sklearn.base import BaseEstimator, clone
  from sklearn.neighbors import KernelDensity
  from sklearn.metrics import pairwise_distances
  from sklearn.model_selection import GridSearchCV, KFold
  from sklearn.utils import check_random_state
  from eli5.utils import vstack
  from eli5.lime.utils import rbf
  from .textutils import generate_samples, DEFAULT_TOKEN_PATTERN, TokenizedText
label_desc: Use image node_id 2 for calling the MultivariateKernelDensitySampler obj's underlying member method code with example usage: obj.sample_near(doc, n_samples) and returns: samples,  self