class_docstr: CP Decomposition via Robust Tensor Power Iteration
  
  Parameters
  ----------
  tensor : tl.tensor
      input tensor to decompose
  rank : int
      rank of the decomposition (number of rank-1 components)
  n_repeat : int, default is 10
      number of initializations to be tried
  n_iteration : int, default is 10
      number of power iterations
  verbose : bool
      level of verbosity
  
  Returns
  -------
  (weights, factors)
  
  weights : 1-D tl.tensor of length `rank`
      contains the eigenvalue of each eigenvector
  factors : list of 2-D tl.tensor of shape (size, rank)
      Each column of each factor corresponds to one eigenvector
code: def __repr__(self):
      return f"Rank-{self.rank} CP decomposition via Robust Tensor Power Iteration."
  
file_path: tensorly/tensorly/decomposition/_cp_power.py
imports: import tensorly
  from ._base_decomposition import DecompositionMixin
  from ..cp_tensor import validate_cp_rank
  from tensorly.tenalg import outer
  import numpy
label_desc: Use image node_id 3 for calling the CPPower obj's underlying member method code with example usage: obj.__repr__() and returns: str, self, str