class_docstr: Symmetric CP Decomposition via Robust Symmetric Tensor Power Iteration
  
  Parameters
  ----------
  rank : int
      rank of the decomposition (number of rank-1 components)
  n_repeat : int, default is 10
      number of initializations to be tried
  n_iterations : int, default is 10
      number of power iterations
  verbose : bool
      level of verbosity
  
  Returns
  -------
  (weights, factor)
  
  weights : 1-D tl.tensor of length `rank`
      contains the eigenvalue of each eigenvector
  factor : 2-D tl.tensor of shape (size, rank)
      each column corresponds to one eigenvector
class_docstr_tok: ["Symmetric","CP","Decomposition","via","Robust","Symmetric","Tensor","Power","Iteration","Parameters","--","--","--","--","--","rank",":","int","rank","of","the","decomposition","(","number","of","rank-1","components",")","n_repeat",":","int",",","default","is","10","number","of","initializations","to","be","tried","n_iterations",":","int",",","default","is","10","number","of","power","iterations","verbose",":","bool","level","of","verbosity","Returns","--","--","--","-","(","weights",",","factor",")","weights",":","1-D","tl.tensor","of","length","`","rank","`","contains","the","eigenvalue","of","each","eigenvector","factor",":","2-D","tl.tensor","of","shape","(","size",",","rank",")","each","column","corresponds","to","one","eigenvector"]
code: def fit_transform(self, tensor):
      self.decomposition_ = symmetric_parafac_power_iteration(
          tensor,
          self.rank,
          n_repeat=self.n_repeat,
          n_iteration=self.n_iteration,
          verbose=self.verbose,
      )
      return self.decomposition_
  
file_path: tensorly/tensorly/decomposition/_symmetric_cp.py
imports: import tensorly
  from ._base_decomposition import DecompositionMixin
  from tensorly.tenalg import outer
  import numpy
  from ..cp_tensor import validate_cp_rank
label_desc: Use image node_id 2 for calling the SymmetricCP obj's underlying member method code with example usage: obj.fit_transform(tensor) and returns: self