class_docstr: Feature selector that removes all low-variance features.
  
  This feature selection algorithm looks only at the features (X), not the
  desired outputs (y), and can thus be used for unsupervised learning.
  
  Read more in the :ref:`User Guide <variance_threshold>`.
  
  Parameters
  ----------
  threshold : float, optional
      Features with a training-set variance lower than this threshold will
      be removed. The default is to keep all features with non-zero variance,
      i.e. remove the features that have the same value in all samples.
  
  Attributes
  ----------
  variances_ : array, shape (n_features,)
      Variances of individual features.
  
  Examples
  --------
  The following dataset has integer features, two of which are the same
  in every sample. These are removed with the default setting for threshold::
  
      >>> X = [[0, 2, 0, 3], [0, 1, 4, 3], [0, 1, 1, 3]]
      >>> selector = VarianceThreshold()
      >>> selector.fit_transform(X)
      array([[2, 0],
             [1, 4],
             [1, 1]])
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code: def _get_support_mask(self):
      check_is_fitted(self, "variances_")
  
      return self.variances_ > self.threshold
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/feature_selection/variance_threshold.py
imports: import numpy
  from ..base import BaseEstimator
  from .base import SelectorMixin
  from ..utils import check_array
  from ..utils.sparsefuncs import mean_variance_axis
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 3 for calling the VarianceThreshold obj's underlying member method code with example usage: obj._get_support_mask() and returns: unknown