class_docstr: Drops features which only have 1 unique value or which have nearly no repeated values (based on max_unique_ratio) and are of category or object type.
class_docstr_tok: ["Drops","features","which","only","have","1","unique","value","or","which","have","nearly","no","repeated","values","(","based","on","max_unique_ratio",")","and","are","of","category","or","object","type","."]
code: def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict):
      features_to_drop = self._drop_unique_features(
          X,
          self.feature_metadata_in,
          max_unique_ratio=self.max_unique_ratio,
      )
      self._remove_features_in(features_to_drop)
      X_out = X[self.features_in]
      return X_out, self.feature_metadata_in.type_group_map_special
  
file_path: autogluon/features/src/autogluon/features/generators/drop_unique.py
imports: import logging
  from pandas import DataFrame
  from autogluon.common.features.feature_metadata import FeatureMetadata
  from autogluon.common.features.types import R_CATEGORY, R_OBJECT, S_IMAGE_BYTEARRAY, S_IMAGE_PATH, S_TEXT
  from .abstract import AbstractFeatureGenerator
label_desc: Use image node_id 2 for calling the DropUniqueFeatureGenerator obj's underlying member method code with example usage: obj._fit_transform(X) and returns: X_out,  self