class_docstr: CP tensor regression
  
  Learns a low rank CP tensor weight, This performs a partial least square regression to a tensor X (>= 2 modes)
  against a matrix Y. The first modes of X and Y will be considered coupled, and the decomposition will maximize
  the covariance between them.
  
  Parameters
  ----------
  n_components : int
      rank of the CP decomposition of the regression weights
  tol : float
      convergence value
  n_iter_max : int, optional, default is 100
      maximum number of iteration
  random_state : None, int or RandomState, optional, default is None
  verbose : bool, default is False
      whether to be verbose during fitting
  
  References
  ----------
  .. [1] Rasmus Bro, "Multiway calibration. Multilinear PLS", Chemometrics, 1996
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docstr: Sets the value of the provided parameters
docstr_tok: ["Sets","the","value","of","the","provided","parameters"]
code: def set_params(self, **parameters):
      """Sets the value of the provided parameters"""
      for parameter, value in parameters.items():
          setattr(self, parameter, value)
      return self
  
file_path: tensorly/tensorly/regression/cp_plsr.py
imports: from ..tenalg import multi_mode_dot, outer
  from ..None import backend
  from ..None import tensor_to_vec
  from ..decomposition._cp import initialize_cp
label_desc: Use image node_id 3 for calling the CP_PLSR obj's underlying member method code with example usage: obj.set_params() and returns: self