docstr: Checks if the basis array is well constructed or constructs it based
  on one of the available options.
docstr_tok: ["Checks","if","the","basis","array","is","well","constructed","or","constructs","it","based","on","one","of","the","available","options","."]
code: def _initialize_basis(self, triplets, X):
      """Checks if the basis array is well constructed or constructs it based
      on one of the available options.
      """
      n_features = X.shape[1]
  
      if isinstance(self.basis, np.ndarray):
          # TODO: should copy?
          basis = check_array(self.basis, copy=True)
          if basis.shape[1] != n_features:
              raise ValueError(
                  "The dimensionality ({}) of the provided bases must"
                  " match the dimensionality of the data "
                  "({}).".format(basis.shape[1], n_features)
              )
      elif self.basis not in self._authorized_basis:
          raise ValueError(
              "`basis` must be one of the options '{}' "
              "or an array of shape (n_basis, n_features).".format(
                  "', '".join(self._authorized_basis)
              )
          )
      if self.basis == "triplet_diffs":
          basis, n_basis = self._generate_bases_dist_diff(triplets, X)
  
      return basis, n_basis
  
imports: from __future__ import print_function, absolute_import, division
  import numpy
  from .base_metric import _TripletsClassifierMixin, MahalanobisMixin
  from ._util import components_from_metric
  from sklearn.base import TransformerMixin
  from .constraints import Constraints
  from sklearn.preprocessing import normalize
  from sklearn.neighbors import NearestNeighbors
  from sklearn.cluster import KMeans
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from sklearn.utils import check_array, check_random_state
  import warnings
label_desc: Use image node_id 6 for calling the _BaseSCML obj's underlying member method code with example usage: obj._initialize_basis(triplets, X) and returns: basis,  n_basis