code: def postprocess_augmented(X_aug, X):
      """Drops rows from augmented data that are duplicated (including duplicates that appeared in original data X)."""
      X_aug = pd.concat([X, X_aug])
      X_aug.drop_duplicates(keep="first", inplace=True)
      X_aug = X_aug.tail(len(X_aug) - len(X))
      logger.log(
          15,
          f"Augmented training dataset with {len(X_aug)} extra datapoints",
      )
      return X_aug.reset_index(drop=True, inplace=False)
  
file_path: autogluon/core/src/autogluon/core/augmentation/distill_utils.py
imports: import gc
  import logging
  import numpy
  import pandas
  from sklearn.neighbors import NearestNeighbors
  from autogluon.common.features.feature_metadata import FeatureMetadata
  from autogluon.common.features.types import R_CATEGORY, R_FLOAT, R_INT
  from ..constants import BINARY, MULTICLASS, REGRESSION
  from ..metrics import mean_squared_error
label_desc: Use image node_id 3 for calling a global function with example usage: postprocess_augmented(X_aug, X) and returns: X_aug