class_docstr: Minimizes memory usage of category features by converting the category values to monotonically increasing int values.
  This is important for category features with string values which can take up significant memory despite the string information not being used downstream.
class_docstr_tok: ["Minimizes","memory","usage","of","category","features","by","converting","the","category","values","to","monotonically","increasing","int","values",".","This","is","important","for","category","features","with","string","values","which","can","take","up","significant","memory","despite","the","string","information","not","being","used","downstream","."]
code: def _fit_transform(self, X: DataFrame, **kwargs) -> (DataFrame, dict):
      self._category_maps = self._get_category_map(X=X)
  
      X_out = self._transform(X)
      return X_out, self.feature_metadata_in.type_group_map_special
  
imports: import logging
  import numpy
  from pandas import DataFrame, RangeIndex
  from autogluon.common.features.types import R_CATEGORY, R_INT
  from ..utils import clip_and_astype
  from .None import AbstractFeatureGenerator
label_desc: Use image node_id 1 for calling the CategoryMemoryMinimizeFeatureGenerator obj's underlying member method code with example usage: obj._fit_transform(X) and returns: X_out,  self