class_docstr: Class for ranking of paths in subgraph
class_docstr_tok: ["Class","for","ranking","of","paths","in","subgraph"]
code: def __call__(
      self,
      questions_batch: List[str],
      template_type_batch: List[str],
      raw_answers_batch: List[List[Tuple[str]]],
      entity_substr_batch: List[List[str]],
      template_answers_batch: List[str],
  ) -> List[str]:
      answers_batch, outp_confidences_batch, answer_ids_batch = (
          [],
          [],
          [],
      )
      entities_and_rels_batch, queries_batch, triplets_batch = (
          [],
          [],
          [],
      )
      for (
          question,
          template_type,
          raw_answers,
          entities,
          template_answer,
      ) in zip(
          questions_batch,
          template_type_batch,
          raw_answers_batch,
          entity_substr_batch,
          template_answers_batch,
      ):
          answers_with_scores = []
          (
              l_questions,
              l_rels,
              l_rels_labels,
              l_cur_answers,
              l_entities,
              l_types,
              l_sparql_queries,
              l_triplets,
              l_confs,
          ) = self.preprocess_ranking_input(question, raw_answers)
  
          n_batches = len(l_questions) // self.batch_size + int(
              len(l_questions) % self.batch_size > 0
          )
          for i in range(n_batches):
              if self.rank:
                  if self.nll_path_ranking:
                      probas = self.ranker(
                          [l_questions[0]],
                          [
                              l_rels_labels[
                                  self.batch_size
                                  * i : self.batch_size
                                  * (i + 1)
                              ]
                          ],
                      )
                      probas = probas[0]
                  else:
                      probas = self.ranker(
                          l_questions[
                              self.batch_size
                              * i : self.batch_size
                              * (i + 1)
                          ],
                          l_rels_labels[
                              self.batch_size
                              * i : self.batch_size
                              * (i + 1)
                          ],
                      )
                      probas = [proba[0] for proba in probas]
              else:
                  probas = [
                      rel_conf
                      for rel_conf, entity_conf in l_confs[
                          self.batch_size
                          * i : self.batch_size
                          * (i + 1)
                      ]
                  ]
              for j in range(
                  self.batch_size * i, self.batch_size * (i + 1)
              ):
                  if j < len(l_cur_answers) and (
                      probas[j - self.batch_size * i] > self.rel_thres
                      or (
                          len(l_rels[j]) > 1
                          and not set(l_rels[j]).intersection(
                              self.type_rels
                          )
                      )
                  ):
                      answers_with_scores.append(
                          (
                              l_cur_answers[j],
                              l_sparql_queries[j],
                              l_triplets[j],
                              l_entities[j],
                              l_types[j],
                              l_rels_labels[j],
                              l_rels[j],
                              round(probas[j - self.batch_size * i], 3),
                              round(l_confs[j][0], 3),
                              l_confs[j][1],
                          )
                      )
          answers_with_scores = sorted(
              answers_with_scores,
              key=lambda x: x[-1] * x[-3],
              reverse=True,
          )
          if (
              template_type == "simple_boolean"
              and not answers_with_scores
          ):
              answers_with_scores = [
                  (["No"], "", [], [], [], [], [], 1.0, 1.0, 1.0)
              ]
          (
              res_answers_list,
              res_answer_ids_list,
              res_confidences_list,
              res_entities_and_rels_list,
          ) = ([], [], [], [])
          res_queries_list, res_triplets_list = [], []
          for n, ans_sc_elem in enumerate(answers_with_scores):
              (
                  init_answer_ids,
                  query,
                  triplets,
                  q_entities,
                  q_types,
                  _,
                  q_rels,
                  p_conf,
                  r_conf,
                  e_conf,
              ) = ans_sc_elem
              answer_ids = []
              for answer_id in init_answer_ids:
                  answer_id = (
                      str(answer_id).replace("@en", "").strip('"')
                  )
                  if answer_id not in answer_ids:
                      answer_ids.append(answer_id)
  
              if self.top_possible_answers > 0:
                  answer_ids = answer_ids[: self.top_possible_answers]
              answer_ids_input = [
                  (answer_id, question) for answer_id in answer_ids
              ]
              answer_ids = [
                  str(answer_id).split("/")[-1]
                  for answer_id in answer_ids
              ]
              parser_info_list = [
                  "find_label" for _ in answer_ids_input
              ]
              init_answer_labels = self.wiki_parser(
                  parser_info_list, answer_ids_input
              )
              if n < 7:
                  log.debug(
                      f"answers: {init_answer_ids[:3]} --- query {query} --- entities {q_entities} --- "
                      f"types {q_types[:3]} --- q_rels {q_rels} --- {ans_sc_elem[5:]} --- "
                      f"answer_labels {init_answer_labels[:3]}"
                  )
              answer_labels = []
              for label in init_answer_labels:
                  if label not in answer_labels:
                      answer_labels.append(label)
              answer_labels = [
                  label
                  for label in answer_labels
                  if (label and label != "Not Found")
              ][:5]
              answer_labels = [str(label) for label in answer_labels]
              if len(answer_labels) > 2:
                  answer = f"{', '.join(answer_labels[:-1])} and {answer_labels[-1]}"
              else:
                  answer = ", ".join(answer_labels)
  
              if "sentence_answer" in self.return_elements:
                  try:
                      answer = sentence_answer(
                          question, answer, entities, template_answer
                      )
                  except ValueError as e:
                      log.warning(f"Error in sentence answer, {e}")
  
              res_answers_list.append(answer)
              res_answer_ids_list.append(answer_ids)
              if "several_confidences" in self.return_elements:
                  res_confidences_list.append((p_conf, r_conf, e_conf))
              else:
                  res_confidences_list.append(p_conf)
              res_entities_and_rels_list.append(
                  [q_entities[:-1], q_rels]
              )
              res_queries_list.append(query)
              res_triplets_list.append(triplets)
  
          if self.top_n == 1:
              if answers_with_scores:
                  answers_batch.append(res_answers_list[0])
                  outp_confidences_batch.append(res_confidences_list[0])
                  answer_ids_batch.append(res_answer_ids_list[0])
                  entities_and_rels_batch.append(
                      res_entities_and_rels_list[0]
                  )
                  queries_batch.append(res_queries_list[0])
                  triplets_batch.append(res_triplets_list[0])
              else:
                  answers_batch.append("Not Found")
                  outp_confidences_batch.append(0.0)
                  answer_ids_batch.append([])
                  entities_and_rels_batch.append([])
                  queries_batch.append([])
                  triplets_batch.append([])
          else:
              answers_batch.append(res_answers_list[: self.top_n])
              outp_confidences_batch.append(
                  res_confidences_list[: self.top_n]
              )
              answer_ids_batch.append(res_answer_ids_list[: self.top_n])
              entities_and_rels_batch.append(
                  res_entities_and_rels_list[: self.top_n]
              )
              queries_batch.append(res_queries_list[: self.top_n])
              triplets_batch.append(res_triplets_list[: self.top_n])
  
      answer_tuple = (answers_batch,)
      if "confidences" in self.return_elements:
          answer_tuple += (outp_confidences_batch,)
      if "answer_ids" in self.return_elements:
          answer_tuple += (answer_ids_batch,)
      if "entities_and_rels" in self.return_elements:
          answer_tuple += (entities_and_rels_batch,)
      if "queries" in self.return_elements:
          answer_tuple += (queries_batch,)
      if "triplets" in self.return_elements:
          answer_tuple += (triplets_batch,)
  
      return answer_tuple
  
file_path: DeepPavlov/deeppavlov/models/kbqa/rel_ranking_infer.py
imports: from logging import getLogger
  from typing import Tuple, List, Any, Optional
  from scipy.special import softmax
  from deeppavlov.core.common.chainer import Chainer
  from deeppavlov.core.common.file import load_pickle, read_json
  from deeppavlov.core.common.registry import register
  from deeppavlov.core.models.component import Component
  from deeppavlov.core.models.serializable import Serializable
  from deeppavlov.models.kbqa.sentence_answer import sentence_answer
  from deeppavlov.models.kbqa.wiki_parser import WikiParser
label_desc: Use image node_id 4 for calling the RelRankerInfer obj's underlying member method code with example usage: obj.__call__(questions_batch, template_type_batch, raw_answers_batch, entity_substr_batch, template_answers_batch) and returns: answer_tuple