code: def tucker_to_tensor(
      tucker_tensor, skip_factor=None, transpose_factors=False
  ):
      """Converts the Tucker tensor into a full tensor
  
      Parameters
      ----------
      tucker_tensor : tl.TuckerTensor or (core, factors)
          core tensor and list of factor matrices
      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 lengh 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
         full tensor of shape ``(factors[0].shape[0], ..., factors[-1].shape[0])``
      """
      core, factors = tucker_tensor
      return multi_mode_dot(
          core, factors, skip=skip_factor, transpose=transpose_factors
      )
  
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 2 for calling a global function with example usage: tucker_to_tensor(tucker_tensor, skip_factor, transpose_factors) and returns: multi_mode_dot