class_docstr: Locally Linear Embedding
  
  Read more in the :ref:`User Guide <locally_linear_embedding>`.
  
  Parameters
  ----------
  n_neighbors : integer
      number of neighbors to consider for each point.
  
  n_components : integer
      number of coordinates for the manifold
  
  reg : float
      regularization constant, multiplies the trace of the local covariance
      matrix of the distances.
  
  eigen_solver : string, {'auto', 'arpack', 'dense'}
      auto : algorithm will attempt to choose the best method for input data
  
      arpack : use arnoldi iteration in shift-invert mode.
                  For this method, M may be a dense matrix, sparse matrix,
                  or general linear operator.
                  Warning: ARPACK can be unstable for some problems.  It is
                  best to try several random seeds in order to check results.
  
      dense  : use standard dense matrix operations for the eigenvalue
                  decomposition.  For this method, M must be an array
                  or matrix type.  This method should be avoided for
                  large problems.
  
  tol : float, optional
      Tolerance for 'arpack' method
      Not used if eigen_solver=='dense'.
  
  max_iter : integer
      maximum number of iterations for the arpack solver.
      Not used if eigen_solver=='dense'.
  
  method : string ('standard', 'hessian', 'modified' or 'ltsa')
      standard : use the standard locally linear embedding algorithm.  see
                 reference [1]
      hessian  : use the Hessian eigenmap method. This method requires
                 ``n_neighbors > n_components * (1 + (n_components + 1) / 2``
                 see reference [2]
      modified : use the modified locally linear embedding algorithm.
                 see reference [3]
      ltsa     : use local tangent space alignment algorithm
                 see reference [4]
  
  hessian_tol : float, optional
      Tolerance for Hessian eigenmapping method.
      Only used if ``method == 'hessian'``
  
  modified_tol : float, optional
      Tolerance for modified LLE method.
      Only used if ``method == 'modified'``
  
  neighbors_algorithm : string ['auto'|'brute'|'kd_tree'|'ball_tree']
      algorithm to use for nearest neighbors search,
      passed to neighbors.NearestNeighbors instance
  
  random_state: numpy.RandomState or int, optional
      The generator or seed used to determine the starting vector for arpack
      iterations.  Defaults to numpy.random.
  
  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_vectors_ : array-like, shape [n_components, n_samples]
      Stores the embedding vectors
  
  reconstruction_error_ : float
      Reconstruction error associated with `embedding_vectors_`
  
  nbrs_ : NearestNeighbors object
      Stores nearest neighbors instance, including BallTree or KDtree
      if applicable.
  
  References
  ----------
  
  .. [1] `Roweis, S. & Saul, L. Nonlinear dimensionality reduction
      by locally linear embedding.  Science 290:2323 (2000).`
  .. [2] `Donoho, D. & Grimes, C. Hessian eigenmaps: Locally
      linear embedding techniques for high-dimensional data.
      Proc Natl Acad Sci U S A.  100:5591 (2003).`
  .. [3] `Zhang, Z. & Wang, J. MLLE: Modified Locally Linear
      Embedding Using Multiple Weights.`
      http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.70.382
  .. [4] `Zhang, Z. & Zha, H. Principal manifolds and nonlinear
      dimensionality reduction via tangent space alignment.
      Journal of Shanghai Univ.  8:406 (2004)`
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docstr: Compute the embedding vectors for data X
  
  Parameters
  ----------
  X : array-like of shape [n_samples, n_features]
      training set.
  
  Returns
  -------
  self : returns an instance of self.
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code: def fit(self, X, y=None):
      """Compute the embedding vectors for data X
  
      Parameters
      ----------
      X : array-like of shape [n_samples, n_features]
          training set.
  
      Returns
      -------
      self : returns an instance of self.
      """
      self._fit_transform(X)
      return self
  
imports: import numpy
  from scipy.linalg import eigh, svd, qr, solve
  from scipy.sparse import eye, csr_matrix
  from ..base import BaseEstimator, TransformerMixin
  from ..utils import check_random_state, check_array
  from ..utils.arpack import eigsh
  from ..utils.validation import check_is_fitted
  from ..utils.validation import FLOAT_DTYPES
  from ..neighbors import NearestNeighbors
label_desc: Use image node_id 3 for calling the LocallyLinearEmbedding obj's underlying member method code with example usage: obj.fit(X, y) and returns: self