class_docstr: Base class for spectral biclustering.
docstr: Creates a biclustering for X.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
docstr_tok: ["Creates","a","biclustering","for","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape","(","n_samples",",","n_features",")"]
code: def fit(self, X):
      """Creates a biclustering for X.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
  
      """
      X = check_array(X, accept_sparse="csr", dtype=np.float64)
      self._check_parameters()
      self._fit(X)
      return self
  
imports: from abc import ABCMeta, abstractmethod
  import numpy
  from scipy.sparse import dia_matrix
  from scipy.sparse import issparse
  from .None import KMeans, MiniBatchKMeans
  from ..base import BaseEstimator, BiclusterMixin
  from ..externals import six
  from ..utils import check_random_state
  from ..utils.arpack import eigsh, svds
  from ..utils.extmath import make_nonnegative, norm, randomized_svd, safe_sparse_dot
  from ..utils.validation import assert_all_finite, check_array
label_desc: Use image node_id 3 for calling the BaseSpectral obj's underlying member method code with example usage: obj.fit(X) and returns: self