class_docstr: Spectral biclustering (Kluger, 2003).
  
  Partitions rows and columns under the assumption that the data has
  an underlying checkerboard structure. For instance, if there are
  two row partitions and three column partitions, each row will
  belong to three biclusters, and each column will belong to two
  biclusters. The outer product of the corresponding row and column
  label vectors gives this checkerboard structure.
  
  Read more in the :ref:`User Guide <spectral_biclustering>`.
  
  Parameters
  ----------
  n_clusters : int or tuple (n_row_clusters, n_column_clusters), default=3
      The number of row and column clusters in the checkerboard
      structure.
  
  method : {'bistochastic', 'scale', 'log'}, default='bistochastic'
      Method of normalizing and converting singular vectors into
      biclusters. May be one of 'scale', 'bistochastic', or 'log'.
      The authors recommend using 'log'. If the data is sparse,
      however, log normalization will not work, which is why the
      default is 'bistochastic'.
  
      .. warning::
         if `method='log'`, the data must not be sparse.
  
  n_components : int, default=6
      Number of singular vectors to check.
  
  n_best : int, default=3
      Number of best singular vectors to which to project the data
      for clustering.
  
  svd_method : {'randomized', 'arpack'}, default='randomized'
      Selects the algorithm for finding singular vectors. May be
      'randomized' or 'arpack'. If 'randomized', uses
      :func:`~sklearn.utils.extmath.randomized_svd`, which may be faster
      for large matrices. If 'arpack', uses
      `scipy.sparse.linalg.svds`, which is more accurate, but
      possibly slower in some cases.
  
  n_svd_vecs : int, default=None
      Number of vectors to use in calculating the SVD. Corresponds
      to `ncv` when `svd_method=arpack` and `n_oversamples` when
      `svd_method` is 'randomized`.
  
  mini_batch : bool, default=False
      Whether to use mini-batch k-means, which is faster but may get
      different results.
  
  init : {'k-means++', 'random'} or ndarray of shape (n_clusters, n_features),             default='k-means++'
      Method for initialization of k-means algorithm; defaults to
      'k-means++'.
  
  n_init : int, default=10
      Number of random initializations that are tried with the
      k-means algorithm.
  
      If mini-batch k-means is used, the best initialization is
      chosen and the algorithm runs once. Otherwise, the algorithm
      is run for each initialization and the best solution chosen.
  
  random_state : int, RandomState instance, default=None
      Used for randomizing the singular value decomposition and the k-means
      initialization. Use an int to make the randomness deterministic.
      See :term:`Glossary <random_state>`.
  
  Attributes
  ----------
  rows_ : array-like of shape (n_row_clusters, n_rows)
      Results of the clustering. `rows[i, r]` is True if
      cluster `i` contains row `r`. Available only after calling ``fit``.
  
  columns_ : array-like of shape (n_column_clusters, n_columns)
      Results of the clustering, like `rows`.
  
  row_labels_ : array-like of shape (n_rows,)
      Row partition labels.
  
  column_labels_ : array-like of shape (n_cols,)
      Column partition labels.
  
  biclusters_ : tuple of two ndarrays
      The tuple contains the `rows_` and `columns_` arrays.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  SpectralCoclustering : Spectral Co-Clustering algorithm (Dhillon, 2001).
  
  References
  ----------
  
  * :doi:`Kluger, Yuval, et. al., 2003. Spectral biclustering of microarray
    data: coclustering genes and conditions.
    <10.1101/gr.648603>`
  
  Examples
  --------
  >>> from sklearn.cluster import SpectralBiclustering
  >>> import numpy as np
  >>> X = np.array([[1, 1], [2, 1], [1, 0],
  ...               [4, 7], [3, 5], [3, 6]])
  >>> clustering = SpectralBiclustering(n_clusters=2, random_state=0).fit(X)
  >>> clustering.row_labels_
  array([1, 1, 1, 0, 0, 0], dtype=int32)
  >>> clustering.column_labels_
  array([1, 0], dtype=int32)
  >>> clustering
  SpectralBiclustering(n_clusters=2, random_state=0)
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code: def __init__(
      self,
      n_clusters=3,
      *,
      method="bistochastic",
      n_components=6,
      n_best=3,
      svd_method="randomized",
      n_svd_vecs=None,
      mini_batch=False,
      init="k-means++",
      n_init=10,
      random_state=None,
  ):
      super().__init__(
          n_clusters,
          svd_method,
          n_svd_vecs,
          mini_batch,
          init,
          n_init,
          random_state,
      )
      self.method = method
      self.n_components = n_components
      self.n_best = n_best
  
imports: from abc import ABCMeta, abstractmethod
  from numbers import Integral
  import numpy
  from scipy.linalg import norm
  from scipy.sparse import dia_matrix, issparse
  from scipy.sparse.linalg import eigsh, svds
  from ..base import BaseEstimator, BiclusterMixin, _fit_context
  from ..utils import check_random_state, check_scalar
  from ..utils._param_validation import Interval, StrOptions
  from ..utils.extmath import make_nonnegative, randomized_svd, safe_sparse_dot
  from ..utils.validation import assert_all_finite
  from ._kmeans import KMeans, MiniBatchKMeans
label_desc: Use image node_id 1 to create a new SpectralBiclustering object from inherited base classes: BaseSpectral with example: obj = SpectralBiclustering(n_clusters)