class_docstr: A batch command processes a set of results at once.
  
  Note that a "batch" represents a set of results coming from a single prompt,
  as the model may produce more-than-one result for a prompt.
class_docstr_tok: ["A","batch","command","processes","a","set","of","results","at","once",".","Note","that","a","``","batch","''","represents","a","set","of","results","coming","from","a","single","prompt",",","as","the","model","may","produce","more-than-one","result","for","a","prompt","."]
code: def run(
      self,
      rows: Sequence[llmfn_output_row.LLMFnOutputRowView],
  ) -> Sequence[llmfn_output_row.LLMFnOutputRow]:
      new_row_indices = self._fn(rows)
      if len(set(new_row_indices)) != len(new_row_indices):
          raise llmfn_post_process.PostProcessExecutionError(
              'Error executing "{}": returned indices should be unique'.format(
                  self._name
              )
          )
  
      new_rows: list[llmfn_output_row.LLMFnOutputRow] = []
      for idx in new_row_indices:
          if idx < 0:
              raise llmfn_post_process.PostProcessExecutionError(
                  'Error executing "{}": returned indices must be greater than or'
                  " equal to zero, got {}".format(self._name, idx)
              )
          if idx >= len(rows):
              raise llmfn_post_process.PostProcessExecutionError(
                  'Error executing "{}": returned indices must be less than length of'
                  " rows (={}), got {}".format(
                      self._name, len(rows), idx
                  )
              )
          new_rows.append(_convert_view_to_output_row(rows[idx]))
      return new_rows
  
imports: from __future__ import annotations
  import abc
  from typing import Sequence
  from google.generativeai.notebook.lib import llmfn_output_row
  from google.generativeai.notebook.lib import llmfn_post_process
label_desc: Use image node_id 3 for calling the LLMFnPostProcessReorderCommand obj's underlying member method code with example usage: obj.run(rows) and returns: new_rows