code: def tt_matrix_to_tensor(tt_matrix):
      """Returns the full tensor whose TT-Matrix decomposition is given by 'factors'
  
          Re-assembles 'factors', which represent a tensor in TT-Matrix format
          into the corresponding full tensor
  
      Parameters
      ----------
      factors: list of 4D-arrays
                TT-Matrix factors (known as core) of shape (rank_k, left_dim_k, right_dim_k, rank_{k+1})
  
      Returns
      -------
      output_tensor: ndarray
                     tensor whose TT-Matrix decomposition was given by 'factors'
      """
      ndim = len(tt_matrix)
      order = list(range(0, ndim * 2, 2)) + list(range(1, ndim * 2, 2))
  
      start_in = ord("a")
      start_out = start_in + ndim
      start_rank = start_out + ndim
      factors_idx = []
      for i in range(ndim):
          idx = [
              start_rank + i,
              start_in + i,
              start_out + i,
              start_rank + i + 1,
          ]
          factors_idx.append("".join(chr(j) for j in idx))
  
      out_idx = "".join(
          chr(start_in + i) + chr(start_out + i) for i in range(ndim)
      )
      eq = ",".join(idx for idx in factors_idx) + "->" + out_idx
  
      res = tl.einsum(eq, *tt_matrix)
  
      return tl.transpose(res, order)
  
label_desc: Use image node_id 1 for calling a global function with example usage: tt_matrix_to_tensor(tt_matrix) and returns: tl