code: def populate_rows_and_columns(
      self,
  ) -> Tuple[Tuple[_Row, ...], Tuple[_Column, ...]]:
      rows: List[_Row] = []
      columns: List[_Column] = []
      ordered_results: List[List[Optional[common.Measurement]]] = [
          [None for _ in self.column_keys] for _ in self.row_keys
      ]
      row_position = {key: i for i, key in enumerate(self.row_keys)}
      col_position = {key: i for i, key in enumerate(self.column_keys)}
      for r in self.results:
          i = row_position[self.row_fn(r)]
          j = col_position[self.col_fn(r)]
          ordered_results[i][j] = r
  
      unique_envs = {r.env for r in self.results}
      render_env = len(unique_envs) > 1
      env_str_len = (
          max(len(i) for i in unique_envs) if render_env else 0
      )
  
      row_name_str_len = max(len(r.as_row_name) for r in self.results)
  
      prior_num_threads = -1
      prior_env = ""
      row_group = -1
      rows_by_group: List[List[List[Optional[common.Measurement]]]] = []
      for (num_threads, env, _), row in zip(
          self.row_keys, ordered_results
      ):
          thread_transition = num_threads != prior_num_threads
          if thread_transition:
              prior_num_threads = num_threads
              prior_env = ""
              row_group += 1
              rows_by_group.append([])
          rows.append(
              _Row(
                  results=row,
                  row_group=row_group,
                  render_env=(render_env and env != prior_env),
                  env_str_len=env_str_len,
                  row_name_str_len=row_name_str_len,
                  time_scale=self.time_scale,
                  colorize=self._colorize,
                  num_threads=num_threads
                  if thread_transition
                  else None,
              )
          )
          rows_by_group[-1].append(row)
          prior_env = env
  
      for i in range(len(self.column_keys)):
          grouped_results = [
              tuple(row[i] for row in g) for g in rows_by_group
          ]
          column = _Column(
              grouped_results=grouped_results,
              time_scale=self.time_scale,
              time_unit=self.time_unit,
              trim_significant_figures=self._trim_significant_figures,
              highlight_warnings=self._highlight_warnings,
          )
          columns.append(column)
  
      rows_tuple, columns_tuple = tuple(rows), tuple(columns)
      for ri in rows_tuple:
          ri.register_columns(columns_tuple)
      return rows_tuple, columns_tuple
  
imports: import collections
  import enum
  import itertools
  from typing import DefaultDict, List, Optional, Tuple
  from torch.utils.benchmark.utils import common
  from torch import tensor
label_desc: Use image node_id 4 for calling the Table obj's underlying member method code with example usage: obj.populate_rows_and_columns() and returns: rows_tuple,  columns_tuple