code: def as_column_strings(self):
      concrete_results = [r for r in self._results if r is not None]
      env = f"({concrete_results[0].env})" if self._render_env else ""
      env = env.ljust(self._env_str_len + 4)
      output = ["  " + env + concrete_results[0].as_row_name]
      for m, col in zip(self._results, self._columns or ()):
          if m is None:
              output.append(col.num_to_str(None, 1, None))
          else:
              output.append(
                  col.num_to_str(
                      m.median / self._time_scale,
                      m.significant_figures,
                      m.iqr / m.median if m.has_warnings else None,
                  )
              )
      return output
  
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 3 for calling the _Row obj's underlying member method code with example usage: obj.as_column_strings() and returns: output