class_docstr: CP tensor regression
  
      Learns a low rank CP tensor weight
  
  Parameters
  ----------
  weight_rank : int
      rank of the CP decomposition of the regression weights
  tol : float
      convergence value
  reg_W : int, optional, default is 1
      regularisation on the weights
  n_iter_max : int, optional, default is 100
      maximum number of iteration
  random_state : None, int or RandomState, optional, default is None
  verbose : int, default is 1
      level of verbosity
class_docstr_tok: ["CP","tensor","regression","Learns","a","low","rank","CP","tensor","weight","Parameters","--","--","--","--","--","weight_rank",":","int","rank","of","the","CP","decomposition","of","the","regression","weights","tol",":","float","convergence","value","reg_W",":","int",",","optional",",","default","is","1","regularisation","on","the","weights","n_iter_max",":","int",",","optional",",","default","is","100","maximum","number","of","iteration","random_state",":","None",",","int","or","RandomState",",","optional",",","default","is","None","verbose",":","int",",","default","is","1","level","of","verbosity"]
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_regression.py
imports: import numpy
  from ..base import partial_tensor_to_vec, partial_unfold
  from ..tenalg import khatri_rao
  from ..cp_tensor import cp_to_tensor, cp_to_vec
  from ..None import backend
label_desc: Use image node_id 3 for calling the CPRegressor obj's underlying member method code with example usage: obj.set_params() and returns: self