class_docstr: Transformer that maps dataframes to numpy arrays of floats.
  
  It is in essence a wrapper around sklearn transformers (meta-transformer)
  that automatically infers the type of data, or makes sure the transformer
  encodes it as floating point numbers. It applies one-hot-encoding
  to categorical columns, and 'passthrough' (nothing) to
  numerical columns. The usefulness of this class is that it can
  preprocess different kinds of data (discrete, continuous) to one
  standard format while also remembering what kind of data was inputted.
  An estimator using this preprocessor can use
  this classes' :code:`dist_type_` attribute to know what kind of data
  was originally inputted.
  
  Attributes
  ----------
  in_type_ : Object
      the type of the input data. Should be either a list, DataFrame,
      ndarray, or Series.
  dist_type_ : str
      A string that in dicates the distribution type of the original data.
      It is one of :code:`"binary"`, :code:`"category"`,
      or :code:`"continuous"`
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docstr: Transform y back to X using the inverse transform of the encoder.
docstr_tok: ["Transform","y","back","to","X","using","the","inverse","transform","of","the","encoder","."]
code: def inverse_transform(self, y):
      """Transform y back to X using the inverse transform of the encoder."""
      inverse = None
      if isinstance(self.transformer, str):
          if self.inferred_type_ in ["binary", "multiclass"]:
              inverse = self.transform_.inverse_transform(y)
          else:
              # This is for:
              # self.inferred_type_ in "continuous", "continuous-multioutput",
              #                        "multilabel-indicator"
              inverse = y
  
          if self.input_dim_ == 1:
              inverse = inverse.reshape(-1)
  
          # Because we are kind, we try to translate back to the original data
          # type, but we only support DataFrame, Series, list(, ndarray).
          if self.in_type_ == DataFrame:
              inverse = DataFrame(inverse, columns=self.columns_)
          elif self.in_type_ == Series:
              inverse = Series(inverse)
          elif self.in_type_ == list:
              inverse = inverse.tolist()
      elif self.transformer is None:
          inverse = y
      else:
          inverse = self.transform_.inverse_transform(y)
  
      return inverse
  
file_path: fairlearn/fairlearn/adversarial/_preprocessor.py
imports: import fairlearn.utils._compatibility
  from ._constants import _TYPE_COMPLIANCE_ERROR, _TYPE_CHECK_ERROR, _ARG_ERROR_MESSAGE, _TYPE_UNKNOWN_ERROR, _INVALID_OHE
  from sklearn.utils import check_array
  from sklearn.preprocessing import OneHotEncoder
  from pandas import Series, DataFrame
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.multiclass import type_of_target
  from numpy import all
  from numpy import sum
  from numpy import unique, isin
label_desc: Use image node_id 5 for calling the FloatTransformer obj's underlying member method code with example usage: obj.inverse_transform(y) and returns: inverse