class_docstr: Drops features which are exact duplicates of other features, leaving only one instance of the data.
  
  Parameters
  ----------
  sample_size_init : int, default 500
      The number of rows to sample when doing an initial filter of duplicate feature candidates.
      Usually, the majority of features can be filtered out using this smaller amount of rows which greatly speeds up the computation of the final check.
      If None or greater than the number of rows, no initial filter will occur. This may increase the time to fit immensely for large datasets.
  sample_size_final : int, default 2000
      The number of rows to sample when doing the final filter to determine duplicate features.
      This theoretically can lead to features that are very nearly duplicates but not exact duplicates being removed,
      but should be near impossible in practice.
      If None or greater than the number of rows, will perform exact duplicate detection (most expensive).
      It is recommended to keep this value below 100000 to maintain reasonable fit times.
  **kwargs :
      Refer to :class:`AbstractFeatureGenerator` documentation for details on valid key word arguments.
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code: def _more_tags(self):
      return {"feature_interactions": False}
  
file_path: autogluon/features/src/autogluon/features/generators/drop_duplicates.py
imports: import logging
  from collections import defaultdict
  from typing import Union
  import numpy
  from pandas import DataFrame
  from autogluon.common.features.types import R_BOOL, R_CATEGORY, R_FLOAT, R_INT
  from .abstract import AbstractFeatureGenerator
label_desc: Use image node_id 9 for calling the DropDuplicatesFeatureGenerator obj's underlying member method code with example usage: obj._more_tags() and returns: dict