code: def __init__(
      self,
      max_iter=100,
      max_proj=10000,
      tol=1e-3,
      init="identity",
      diagonal=False,
      diagonal_c=1.0,
      verbose=False,
      preprocessor=None,
      random_state=None,
      convergence_threshold="deprecated",
  ):
      if convergence_threshold != "deprecated":
          warnings.warn(
              '"convergence_threshold" parameter has been '
              ' renamed to "tol". It has been deprecated in'
              " version 0.6.3 and will be removed in 0.7.0"
              "",
              FutureWarning,
          )
          tol = convergence_threshold
      self.convergence_threshold = "deprecated"  # Avoid errors
      self.max_iter = max_iter
      self.max_proj = max_proj
      self.tol = tol
      self.init = init
      self.diagonal = diagonal
      self.diagonal_c = diagonal_c
      self.verbose = verbose
      self.random_state = random_state
      super(_BaseMMC, self).__init__(preprocessor)
  
imports: import numpy
  from sklearn.base import TransformerMixin
  from sklearn.utils.validation import assert_all_finite
  from .base_metric import _PairsClassifierMixin, MahalanobisMixin
  from .constraints import Constraints, wrap_pairs
  from ._util import components_from_metric, _initialize_metric_mahalanobis
  import warnings
label_desc: Use image node_id 1 to create a new _BaseMMC object from inherited base classes: MahalanobisMixin with example: obj = _BaseMMC(max_iter, max_proj, tol, init, diagonal, diagonal_c, verbose, preprocessor, random_state, convergence_threshold)