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
code: def __init__(
      self,
      weight_ranks,
      tol=10e-7,
      reg_W=1,
      n_iter_max=100,
      random_state=None,
      verbose=1,
  ):
      self.weight_ranks = weight_ranks
      self.tol = tol
      self.reg_W = reg_W
      self.n_iter_max = n_iter_max
      self.random_state = random_state
      self.verbose = verbose
  
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 1 to create a new TuckerRegressor object  with example: obj = TuckerRegressor(weight_ranks, tol, reg_W, n_iter_max, random_state, verbose)