class_docstr: Tucker tensor regression
  
      Learns a low rank Tucker weight for the regression
  
  Parameters
  ----------
  weight_ranks : int list
      dimension of each mode of the core Tucker weight
  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
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/tucker_regression.py
imports: import numpy
  from ..base import unfold, vec_to_tensor
  from ..base import partial_tensor_to_vec, partial_unfold
  from ..tenalg import kronecker
  from ..tucker_tensor import tucker_to_tensor, tucker_to_vec
  from ..None import backend
label_desc: Use image node_id 3 for calling the TuckerRegressor obj's underlying member method code with example usage: obj.set_params() and returns: self