code: def generate_single_dataset_final_summary_table(
      correls_by_section: Dict[str, Union[Dict, pandas.DataFrame]],
      baseline_set: str,
  ) -> pandas.DataFrame:
      emb_section_summaries = {}
      for emb_section in ("ROSCOE-SA", "ROSCOE-SS"):
          section_data = []
          section_row_titles = []
          for model, model_df in correls_by_section[
              emb_section
          ].items():
              if "roscoe-512-roberta-base" in model:
                  model_num = "(1)"
              elif "all-mpnet-base-v2" in model:
                  model_num = "(2)"
              elif "sup-simcse-roberta-base" in model:
                  model_num = "(3)"
              else:
                  raise NotImplementedError()
  
              section_data.append(model_df["ANY_ERROR"][emb_section])
              section_row_titles.append(f"{emb_section} {model_num}")
  
          emb_section_summaries[emb_section] = pandas.DataFrame(
              data=section_data,
              index=section_row_titles,
              columns=["ANY_ERROR"],
          )
  
      summary_df = pandas.concat(
          [
              correls_by_section["baselines"].loc[
                  REPORTED_BASELINES[baseline_set]
              ],
              emb_section_summaries["ROSCOE-SA"],
              emb_section_summaries["ROSCOE-SS"],
              correls_by_section["ROSCOE-LI"].loc[["ROSCOE-LI"]],
              correls_by_section["ROSCOE-LC"].loc[["ROSCOE-LC"]],
          ]
      )
      return summary_df
  
imports: import argparse
  from collections import defaultdict
  import csv
  import math
  import os
  import pathlib
  from typing import Dict, Iterable, List, Optional, Set, Tuple, Union
  import pandas
  from projects.roscoe.meta_evaluation.correlations import Analyzer
  from projects.roscoe.score import ROSCOE_SA, ROSCOE_SS, ROSCOE_LI, ROSCOE_LC, SUPERVISED_SCORES, UNSUPERVISED_SCORES
  from projects.roscoe.utils import assert_all_elements_same_length, ordered_union, split_list
  from projects.roscoe.meta_evaluation.table_file_writing import ANNOTATION_COLUMN_ORDER, COLUMN_RENAMES, MODELS, ROSCOE_SECTIONS, STEP_COLUMN_RENAMES, section_rows, write_every_dataset_summary_table_tex, write_every_dataset_final_summary_table_tex, write_granular_summary_tex
label_desc: Use image node_id 29 for calling a global function with example usage: generate_single_dataset_final_summary_table(correls_by_section, baseline_set) and returns: summary_df