code: def __init__(
      self,
      beta=1e-5,
      basis="triplet_diffs",
      n_basis=None,
      gamma=5e-3,
      max_iter=10000,
      output_iter=500,
      batch_size=10,
      verbose=False,
      preprocessor=None,
      random_state=None,
  ):
      self.beta = beta
      self.basis = basis
      self.n_basis = n_basis
      self.gamma = gamma
      self.max_iter = max_iter
      self.output_iter = output_iter
      self.batch_size = batch_size
      self.verbose = verbose
      self.preprocessor = preprocessor
      self.random_state = random_state
      super(_BaseSCML, self).__init__(preprocessor)
  
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 1 to create a new _BaseSCML object from inherited base classes: MahalanobisMixin with example: obj = _BaseSCML(beta, basis, n_basis, gamma, max_iter, output_iter, batch_size, verbose, preprocessor, random_state)