class_docstr: Fills missing values in the data.
  
  Parameters
  ----------
  fillna_map : dict, default {'object': ''}
      Map which dictates the fill values of NaNs.
      Keys are the raw types of the features as in self.feature_metadata_in.type_map_raw.
      If a feature's raw type is not present in fillna_map, its NaN values are filled to fillna_default.
  fillna_default, default np.nan
      The default fillna value if the feature's raw type is not present in fillna_map.
      Be careful about setting this to anything other than np.nan, as not all raw types can handle int, float, or string values.
  inplace : bool, default False
      If True, then the NaN values are filled inplace without copying the input data.
      This will alter the input data outside of the scope of this function.
  **kwargs :
      Refer to :class:`AbstractFeatureGenerator` documentation for details on valid key word arguments.
code: def _transform(self, X: DataFrame) -> DataFrame:
      if self._fillna_feature_map:
          with warnings.catch_warnings():
              warnings.simplefilter(
                  action="ignore", category=FutureWarning
              )
              # FIXME: v1.1 Remove this warning filter and resolve.
              #  In Pandas 2.1, the `downcast` argument was deprecated,
              #  but we need it to avoid incorrect type conversion.
              #  Pandas authors may have not considered our edge-case.
              #  We specifically want to have an object dtype not be converted to a numeric dtype,
              #  even if all of the values can be converted to numeric.
              #  However, without specifying `downcast=False`, it will be converted to numeric, which we don't want.
              #  Note: Non-trivial to keep current functionality without specifying `downcast=False`...
              #  Doing so may end up slowing down the code noticeably.
              if self.inplace:
                  X.fillna(
                      self._fillna_feature_map,
                      inplace=True,
                      downcast=False,
                  )
              else:
                  X = X.fillna(
                      self._fillna_feature_map,
                      inplace=False,
                      downcast=False,
                  )
      return X
  
file_path: autogluon/features/src/autogluon/features/generators/fillna.py
imports: import logging
  import warnings
  import numpy
  from pandas import DataFrame
  from autogluon.common.features.types import R_OBJECT
  from .abstract import AbstractFeatureGenerator
label_desc: Use image node_id 3 for calling the FillNaFeatureGenerator obj's underlying member method code with example usage: obj._transform(X) and returns: X