code: def predict(self, X):
      X = X.astype(np.float32)
      X = np.ascontiguousarray(X)
      if X.ndim == 1:
          X = X[np.newaxis]
      D, I = self.index.search(X, self.n_neighbors)
      outputs = np.squeeze(self.labels[I])
      weights = _get_weights(D, self.weights)
      if weights is None:
          y_pred, _ = mode(outputs, axis=1)
      else:
          y_pred, _ = weighted_mode(outputs, weights, axis=1)
      return y_pred
  
imports: import logging
  import numpy
  from pandas import DataFrame
  from scipy.stats import mode
  from sklearn.utils.extmath import weighted_mode
  from autogluon.common.utils.try_import import try_import_faiss
label_desc: Use image node_id 3 for calling the FAISSNeighborsClassifier obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred