class_docstr: Spectral embedding of SPD matrices into an Euclidean space.
  
  It uses Laplacian Eigenmaps [1]_ to embed SPD matrices into an Euclidean
  space of smaller dimension. The basic hypothesis is that high-dimensional
  data lives in a low-dimensional manifold, whose intrinsic geometry can be
  described via the Laplacian matrix of a graph. The vertices of this graph
  are the SPD matrices and the weights of the links are determined by the
  Riemannian distance between each pair of them.
  
  Parameters
  ----------
  n_components : integer, default=2
      The dimension of the projected subspace.
  metric : string | dict, default='riemann'
      The type of metric to be used for defining pairwise distance between
      SPD matrices.
  eps : None | float, default=None
      The scaling of the Gaussian kernel. If none is given it will use the
      square of the median of pairwise distances between points.
  
  References
  ----------
  .. [1] `Laplacian Eigenmaps for dimensionality
      reduction and data representation
      <https://ieeexplore.ieee.org/document/6789755>`_
      M. Belkin and P. Niyogi, in Neural Computation, vol. 15, no. 6,
      p. 1373-1396 , 2003
docstr: Calculate the coordinates of the embedded points.
  
  Parameters
  ----------
  X : ndarray, shape (n_matrices, n_channels, n_channels)
      Set of SPD matrices.
  y : None
      Not used, here for compatibility with sklearn API.
  
  Returns
  -------
  X_new : ndarray, shape (n_matrices, n_components)
      Coordinates of embedded matrices.
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code: def fit_transform(self, X, y=None):
      """Calculate the coordinates of the embedded points.
  
      Parameters
      ----------
      X : ndarray, shape (n_matrices, n_channels, n_channels)
          Set of SPD matrices.
      y : None
          Not used, here for compatibility with sklearn API.
  
      Returns
      -------
      X_new : ndarray, shape (n_matrices, n_components)
          Coordinates of embedded matrices.
  
      """
      self.fit(X)
      return self.embedding_
  
imports: import numpy
  from scipy.linalg import solve, eigh
  from scipy.sparse import csr_matrix
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.manifold import spectral_embedding
  from .utils.kernel import kernel
  from .utils.distance import pairwise_distance
label_desc: Use image node_id 4 for calling the SpectralEmbedding obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self