code: def _symeig_backward(D_grad, U_grad, A, D, U, largest):
      # if `U` is square, then the columns of `U` is a complete eigenspace
      if U.size(-1) == U.size(-2):
          return _symeig_backward_complete_eigenspace(
              D_grad, U_grad, A, D, U
          )
      else:
          return _symeig_backward_partial_eigenspace(
              D_grad, U_grad, A, D, U, largest
          )
  
imports: from typing import Dict, Optional, Tuple
  import torch
  from torch import Tensor
  from .None import _linalg_utils
  from .overrides import handle_torch_function, has_torch_function
label_desc: Use image node_id 7 for calling a global function with example usage: _symeig_backward(D_grad, U_grad, A, D, U, largest) and returns: _symeig_backward_complete_eigenspace, _symeig_backward_partial_eigenspace