code: def read_and_process_data(
      human_annotation_filename: str,
      baseline_scores_filename: str,
      roscoe_scores_filename: str,
      removed_ids: List[str],
      reference_option: str,
      output_filename: Optional[str] = None,
  ) -> Tuple[Dict[str, List[float]], Set[str], Set[str]]:
      """
      Parses scores scores and annotations from their files, removes designated examples,
      and aggregates scores in various ways.
  
      Returns the processed scores, along with whatever new aggregate score/annotation
      names resulted from the processing.
      """
      rows_base, tags_base = parse_all_scores(
          human_fn=human_annotation_filename,
          baseline_fn=baseline_scores_filename,
          alignment_fn=roscoe_scores_filename,
          removed_ids=removed_ids,
          reference_option=reference_option,
          outfn=output_filename,
      )
      values_by_title = process_values(rows_base, tags_base)
  
      return values_by_title
  
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 35 for calling a global function with example usage: read_and_process_data(human_annotation_filename, baseline_scores_filename, roscoe_scores_filename, removed_ids, reference_option, output_filename) and returns: values_by_title