code: def tucker_to_unfolded(
      tucker_tensor, mode=0, skip_factor=None, transpose_factors=False
  ):
      """Converts the Tucker decomposition into an unfolded tensor (i.e. a matrix)
  
      Parameters
      ----------
      tucker_tensor : tl.TuckerTensor or (core, factors)
          core tensor and list of factor matrices
      mode : None or int list, optional, default is None
      skip_factor : None or int, optional, default is None
          if not None, index of a matrix to skip
          Note that in any case, `modes`, if provided, should have a length of ``tensor.ndim``
      transpose_factors : bool, optional, default is False
          if True, the matrices or vectors in in the list are transposed
  
      Returns
      -------
      2D-array
          unfolded tensor
      """
      return unfold(
          tucker_to_tensor(
              tucker_tensor,
              skip_factor=skip_factor,
              transpose_factors=transpose_factors,
          ),
          mode,
      )
  
imports: from ._factorized_tensor import FactorizedTensor
  from .base import unfold, tensor_to_vec
  from .tenalg import multi_mode_dot, mode_dot
  from .None import backend
  import numpy
  from scipy.optimize import brentq
  import warnings
label_desc: Use image node_id 4 for calling a global function with example usage: tucker_to_unfolded(tucker_tensor, mode, skip_factor, transpose_factors) and returns: unfold