class_docstr: An abstract class for computing distances between neural networks. 
docstr: Adds the current result to all results. 
docstr_tok: ["Adds","the","current","result","to","all","results","."]
code: def _add_to_all_ret(
      cls, curr_ret, i, j, n1, n2, all_ret=None, es_is_iterable=None
  ):
      """Adds the current result to all results."""
      if all_ret is None:
          if hasattr(curr_ret, "__iter__"):
              es_is_iterable = True
              all_ret = [
                  np.zeros((n1, n2)) for _ in range(len(curr_ret))
              ]
          else:
              es_is_iterable = False
              all_ret = np.zeros((n1, n2))
      if es_is_iterable:
          for k in range(len(curr_ret)):
              all_ret[k][i, j] = curr_ret[k]
      else:
          all_ret[i, j] = curr_ret
      return all_ret, es_is_iterable
  
imports: import numpy
  from .None import neural_network
  from ..gp.kernel import ExpSumOfDistsKernel, SumOfExpSumOfDistsKernel
  from ..utils.oper_utils import opt_transport
label_desc: Use image node_id 4 for calling the NNDistanceComputer obj's underlying member method code with example usage: obj._add_to_all_ret(cls, curr_ret, i, j, n1, n2, all_ret, es_is_iterable) and returns: all_ret,  es_is_iterable