code: def main(parameters):
      print("Parameters:", parameters)
      # Read data
      sentences = utils.read_sentences_from_file(
          parameters["path_to_input_file"],
          one_sentence_per_line=parameters["one_sentence_per_line"],
      )
  
      # Identify mentions
      ner_model = NER.get_model(parameters)
      ner_output_data = ner_model.predict(sentences)
      sentences = ner_output_data["sentences"]
      mentions = ner_output_data["mentions"]
  
      output_folder_path = parameters["output_folder_path"]
  
      if (
          (output_folder_path is not None)
          and os.path.exists(output_folder_path)
          and os.listdir(output_folder_path)
      ):
          print(
              "The given output directory ({}) already exists and is not empty.".format(
                  output_folder_path
              )
          )
          answer = input(
              "Would you like to empty the existing directory? [Y/N]\n"
          )
  
          if answer.strip() == "Y":
              print("Deleting {}...".format(output_folder_path))
              shutil.rmtree(output_folder_path)
          else:
              raise ValueError(
                  "Output directory ({}) already exists and is not empty.".format(
                      output_folder_path
                  )
              )
  
      if output_folder_path is not None:
          utils.write_dicts_as_json_per_line(
              sentences,
              utils.get_sentences_txt_file_path(output_folder_path),
          )
          utils.write_dicts_as_json_per_line(
              mentions,
              utils.get_mentions_txt_file_path(output_folder_path),
          )
  
      # Generate candidates and get the data that describes the candidates
      candidate_generator = CG.get_model(parameters)
      candidate_generator.process_mentions_for_candidate_generator(
          sentences=sentences, mentions=mentions
      )
  
      for mention in mentions:
          mention["candidates"] = candidate_generator.get_candidates(
              mention
          )
          if parameters["consider_additional_datafetcher"]:
              data_fetcher = CDF.get_model(parameters)
              for candidate in mention["candidates"]:
                  data_fetcher.get_data_for_entity(candidate)
  
      if output_folder_path is not None:
          utils.write_dicts_as_json_per_line(
              mentions,
              utils.get_mentions_txt_file_path(output_folder_path),
          )
  
      # Reranking
      reranking_model = R.get_model(parameters)
      reranking_model.rerank(mentions, sentences)
  
      if output_folder_path is not None:
          utils.write_dicts_as_json_per_line(
              mentions,
              utils.get_mentions_txt_file_path(output_folder_path),
          )
          utils.write_end2end_pickle_output(
              sentences, mentions, output_folder_path
          )
          utils.present_annotated_sentences(
              sentences,
              mentions,
              utils.get_end2end_pretty_output_file_path(
                  output_folder_path
              ),
          )
  
      # Showcase results
      utils.present_annotated_sentences(sentences, mentions)
  
file_path: BLINK/blink/main_solr.py
imports: import os
  import blink.utils
  import blink.ner
  import blink.candidate_generation
  import blink.candidate_data_fetcher
  import blink.reranker
  import argparse
  import shutil
label_desc: Use image node_id 1 for calling a global function with example usage: main(parameters) without return types