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: Returns a dictionary of parameters
code: def get_params(self, **kwargs):
      """Returns a dictionary of parameters"""
      params = [
          "weight_ranks",
          "tol",
          "reg_W",
          "n_iter_max",
          "random_state",
          "verbose",
      ]
      return {
          param_name: getattr(self, param_name) for param_name in params
      }
  
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 2 for calling the TuckerRegressor obj's underlying member method code with example usage: obj.get_params() and returns: unknown