code: def _predict(
      self, X: pd.DataFrame, y: typing.Optional[pd.DataFrame]
  ) -> pd.DataFrame:
      if y is not None:
          raise ValueError("y must not be provided")
  
      raw_predictions = dict()
      raw_probas = dict()
      for i in range(len(self.target_indices)):
          for j in range(i + 1, len(self.target_indices)):
              if self.models[i][j] is not None:
                  raw_predictions[(i, j)] = self.models[i][j].predict(X)
                  raw_probas[(i, j)] = self.models[i][j].predict_proba(
                      X
                  )
  
      if len(raw_predictions) == 0:
          predictions = pd.DataFrame(
              0, index=X.index, columns=self.strategies_
          ).astype(float)
          predictions.iloc[:, self.single_strategy_idx] = 1.0
          return predictions
  
      predictions = {}
      for x_idx in range(X.shape[0]):
          wins = np.zeros(self.target_indices.shape)
          for i in range(len(self.target_indices)):
              for j in range(i + 1, len(self.target_indices)):
                  if (i, j) in raw_predictions:
                      if self.configuration["prediction"] == "soft":
                          if raw_probas[(i, j)].shape[1] == 1:
                              proba = raw_probas[(i, j)][x_idx][0]
                          else:
                              proba = raw_probas[(i, j)][x_idx][1]
                          wins[i] += proba
                          wins[j] += 1 - proba
                      elif self.configuration["prediction"] == "hard":
                          prediction = raw_predictions[(i, j)][x_idx]
                          if prediction == 1:
                              wins[i] += 1
                          else:
                              wins[j] += 1
                      else:
                          raise ValueError(
                              self.configuration["prediction"]
                          )
  
          n_prev = np.inf
          # Tie breaking
          while True:
              most_wins = np.max(wins)
              most_wins_mask = most_wins == wins
              if n_prev == np.sum(most_wins_mask):
                  n_prev = np.sum(most_wins_mask)
                  hit = False
                  for method in self.tie_break_order:
                      if method not in self.strategies_:
                          continue
                      method_idx = self.strategies_.index(method)
                      if most_wins_mask[method_idx]:
                          wins[method_idx] += 1
                          hit = True
                          break
                  if not hit:
                      wins[
                          int(
                              self.rng.choice(
                                  np.argwhere(most_wins_mask).flatten()
                              )
                          )
                      ] += 1
              elif np.sum(most_wins_mask) > 1:
                  n_prev = np.sum(most_wins_mask)
                  where = np.argwhere(most_wins_mask).flatten()
                  for i, j in itertools.combinations(where, 2):
                      if (i, j) in raw_predictions:
                          prediction = raw_predictions[(i, j)][x_idx]
                          if prediction == 1:
                              wins[i] += 1
                          else:
                              wins[j] += 1
                      else:
                          method_i = self.strategies_[i]
                          method_j = self.strategies_[j]
                          if self.tie_break_order.index(
                              method_i
                          ) < self.tie_break_order.index(method_j):
                              wins[i] += 1
                          else:
                              wins[j] += 1
              else:
                  break
  
          wins = wins / np.sum(wins)
          predictions[X.index[x_idx]] = wins
  
      return_value = {
          task_id: {
              strategy: predictions[task_id][strategy_idx]
              for strategy_idx, strategy in enumerate(self.strategies_)
          }
          for task_id in X.index
      }
      return_value = (
          pd.DataFrame(return_value).transpose().astype(float)
      )
      return_value = return_value[self.strategies_]
      return_value = return_value.fillna(0.0)
      return return_value
  
imports: import typing
  import copy
  import itertools
  import numpy
  import pandas
  import scipy.stats
  import sklearn.ensemble
label_desc: Use image node_id 3 for calling the OneVSOneSelector obj's underlying member method code with example usage: obj._predict(X, y) and returns: return_value, predictions