class_docstr: Spectral embedding for non-linear dimensionality reduction.
  
  Forms an affinity matrix given by the specified function and
  applies spectral decomposition to the corresponding graph laplacian.
  The resulting transformation is given by the value of the
  eigenvectors for each data point.
  
  Read more in the :ref:`User Guide <spectral_embedding>`.
  
  Parameters
  -----------
  n_components : integer, default: 2
      The dimension of the projected subspace.
  
  eigen_solver : {None, 'arpack', 'lobpcg', or 'amg'}
      The eigenvalue decomposition strategy to use. AMG requires pyamg
      to be installed. It can be faster on very large, sparse problems,
      but may also lead to instabilities.
  
  random_state : int seed, RandomState instance, or None, default : None
      A pseudo random number generator used for the initialization of the
      lobpcg eigenvectors decomposition when eigen_solver == 'amg'.
  
  affinity : string or callable, default : "nearest_neighbors"
      How to construct the affinity matrix.
       - 'nearest_neighbors' : construct affinity matrix by knn graph
       - 'rbf' : construct affinity matrix by rbf kernel
       - 'precomputed' : interpret X as precomputed affinity matrix
       - callable : use passed in function as affinity
         the function takes in data matrix (n_samples, n_features)
         and return affinity matrix (n_samples, n_samples).
  
  gamma : float, optional, default : 1/n_features
      Kernel coefficient for rbf kernel.
  
  n_neighbors : int, default : max(n_samples/10 , 1)
      Number of nearest neighbors for nearest_neighbors graph building.
  
  n_jobs : int, optional (default = 1)
      The number of parallel jobs to run.
      If ``-1``, then the number of jobs is set to the number of CPU cores.
  
  Attributes
  ----------
  
  embedding_ : array, shape = (n_samples, n_components)
      Spectral embedding of the training matrix.
  
  affinity_matrix_ : array, shape = (n_samples, n_samples)
      Affinity_matrix constructed from samples or precomputed.
  
  References
  ----------
  
  - A Tutorial on Spectral Clustering, 2007
    Ulrike von Luxburg
    http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.165.9323
  
  - On Spectral Clustering: Analysis and an algorithm, 2011
    Andrew Y. Ng, Michael I. Jordan, Yair Weiss
    http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100
  
  - Normalized cuts and image segmentation, 2000
    Jianbo Shi, Jitendra Malik
    http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.160.2324
docstr: Fit the model from data in X and transform X.
  
  Parameters
  ----------
  X: array-like, shape (n_samples, n_features)
      Training vector, where n_samples is the number of samples
      and n_features is the number of features.
  
      If affinity is "precomputed"
      X : array-like, shape (n_samples, n_samples),
      Interpret X as precomputed adjacency graph computed from
      samples.
  
  Returns
  -------
  X_new: array-like, shape (n_samples, n_components)
docstr_tok: ["Fit","the","model","from","data","in","X","and","transform","X.","Parameters","--","--","--","--","--","X",":","array-like",",","shape","(","n_samples",",","n_features",")","Training","vector",",","where","n_samples","is","the","number","of","samples","and","n_features","is","the","number","of","features",".","If","affinity","is","``","precomputed","''","X",":","array-like",",","shape","(","n_samples",",","n_samples",")",",","Interpret","X","as","precomputed","adjacency","graph","computed","from","samples",".","Returns","--","--","--","-","X_new",":","array-like",",","shape","(","n_samples",",","n_components",")"]
code: def fit_transform(self, X, y=None):
      """Fit the model from data in X and transform X.
  
      Parameters
      ----------
      X: array-like, shape (n_samples, n_features)
          Training vector, where n_samples is the number of samples
          and n_features is the number of features.
  
          If affinity is "precomputed"
          X : array-like, shape (n_samples, n_samples),
          Interpret X as precomputed adjacency graph computed from
          samples.
  
      Returns
      -------
      X_new: array-like, shape (n_samples, n_components)
      """
      self.fit(X)
      return self.embedding_
  
imports: import warnings
  import numpy
  from scipy import sparse
  from scipy.linalg import eigh
  from scipy.sparse.linalg import lobpcg
  from ..base import BaseEstimator
  from ..externals import six
  from ..utils import check_random_state, check_array, check_symmetric
  from ..utils.extmath import _deterministic_vector_sign_flip
  from ..utils.graph import graph_laplacian
  from ..utils.sparsetools import connected_components
  from ..utils.arpack import eigsh
  from ..metrics.pairwise import rbf_kernel
  from ..neighbors import kneighbors_graph
label_desc: Use image node_id 5 for calling the SpectralEmbedding obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self