docstr: Convert the data back to the original representation.
  
  Parameters
  ----------
  X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
      The transformed data.
  
  Returns
  -------
  X_tr : Type is specified by the `output_type` parameter.
      Inverse transformed array.
code: def inverse_transform(self, X):
      """Convert the data back to the original representation.
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
          The transformed data.
  
      Returns
      -------
      X_tr : Type is specified by the `output_type` parameter.
          Inverse transformed array.
      """
      self._check_n_features(X, reset=False)
  
      result = {}
      for feature in self._features:
          Xi = _slice_feat(X, feature)
          inv = self._encoders[feature].inverse_transform(Xi)
          result[feature] = inv
  
      r = DataFrame(result)
      return _get_output(
          self.output_type, self.input_type, r, self.dtype
      )
  
imports: import warnings
  from typing import List, Optional, TypeVar
  import cuml.internals.logger
  from cudf import DataFrame, Series
  from cuml import Base
  from cuml.common.doc_utils import generate_docstring
  from cuml.common.exceptions import NotFittedError
  from cuml.internals.safe_imports import cpu_only_import, gpu_only_import, gpu_only_import_from
  from cuml.preprocessing import LabelEncoder
label_desc: Use image node_id 5 for calling the OrdinalEncoder obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: _get_output