code: def pad_tt_rank(factor_list, n_padding=1, pad_boundaries=False):
      """Pads the factors of a Tensor-Train so as to increase its rank without changing its reconstruction
  
      The tensor-train (ring) will be padded with 0s to increase its rank only but not the underlying tensor it represents.
  
      Parameters
      ----------
      factor_list : tensor list
      n_padding : int, default is 1
          how much to increase the rank (bond dimension) by
      pad_boundaries : bool, default is False
          if True, also pad the boundaries (useful for a tensor-ring)
          should be False for a tensor-train to keep the boundary rank to be 1
  
      Returns
      -------
      padded_factor_list
      """
      new_factors = []
      n_factors = len(factor_list)
  
      for i, factor in enumerate(factor_list):
          n_padding_left = n_padding_right = n_padding
          if (i == 0) and not pad_boundaries:
              n_padding_left = 0
          elif (i == n_factors - 1) and not pad_boundaries:
              n_padding_right = 0
  
          r1, *s, r2 = tl.shape(factor)
          new_factor = tl.zeros(
              (r1 + n_padding_left, *s, r2 + n_padding_right),
              **tl.context(factor),
          )
          new_factors.append(
              tl.index_update(
                  new_factor, tl.index[:r1, ..., :r2], factor
              )
          )
  
      return new_factors
  
imports: import tensorly
  from ._factorized_tensor import FactorizedTensor
  import numpy
  import warnings
label_desc: Use image node_id 7 for calling a global function with example usage: pad_tt_rank(factor_list, n_padding, pad_boundaries) and returns: new_factors