class_docstr: Encode categorical integer features as a one-hot numeric array.
  
  The input to this transformer should be an array-like of integers or
  strings, denoting the values taken on by categorical (discrete) features.
  The features are encoded using a one-hot (aka 'one-of-K' or 'dummy')
  encoding scheme. This creates a binary column for each category and
  returns a sparse matrix or dense array (depending on the ``sparse``
  parameter)
  
  By default, the encoder derives the categories based on the unique values
  in each feature. Alternatively, you can also specify the `categories`
  manually.
  
  Always uses handle_unknown='ignore' which maps unknown test-time categories to all zeros vector.
  
  Parameters
  ----------
  categories : 'auto' or a list of lists/arrays of values, default='auto'.
      Categories (unique values) per feature:
  
      - 'auto' : Determine categories automatically from the training data.
      - list : ``categories[i]`` holds the categories expected in the ith
        column. The passed categories should not mix strings and numeric
        values within a single feature, and should be sorted in case of
        numeric values.
  
      The used categories can be found in the ``categories_`` attribute.
  
  drop : 'first' or a list/array of shape (n_features,), default=None.
      Specifies a methodology to use to drop one of the categories per
      feature. This is useful in situations where perfectly collinear
      features cause problems, such as when feeding the resulting data
      into a neural network or an unregularized regression.
  
      - None : retain all features (the default).
      - 'first' : drop the first category in each feature. If only one
        category is present, the feature will be dropped entirely.
      - array : ``drop[i]`` is the category in feature ``X[:, i]`` that
        should be dropped. If ``drop[i]`` is an infrequent category, an
        error is raised: it is only possible to drop all of the infrequent
        categories, not just one of them.
      - 'infrequent' : drop the infrequent categories column (see
        ``max_levels`` parameter).
  
  sparse : boolean, default=True
      Will return sparse matrix if set True else will return an array.
  
  dtype : number type, default=float
      Desired dtype of output.
  
  max_levels : int, default=None
      One less than the maximum number of categories to keep (max_levels = 2 means we keep 3 distinct categories).
      Infrequent categories are grouped together and mapped into a single column, which counts as extra category.
      Unknown categories encountered at test time are mapped to all zeros vector.
  
  Attributes
  ----------
  categories_ : list of arrays
      The categories of each feature determined during fitting
      (in order of the features in X and corresponding with the output
      of ``transform``). This includes the category specified in ``drop``
      (if any).
  
  drop_idx_ : array of shape (n_features,)
      ``drop_idx_[i]`` is the index in ``categories_[i]`` of the category to
      be dropped for each feature. None if all the transformed features will
      be retained.
  
  infrequent_indices_: list of arrays of shape(n_infrequent_categories)
      ``infrequent_indices_[i]`` contains a list of indices in
      ``categories_[i]`` corresponding to the infrequent categories.
docstr: Convert the back data to the original representation.
  
  In case unknown categories are encountered (all zeros in the
  one-hot encoding), ``None`` is used to represent this category.
  
  Parameters
  ----------
  X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
      The transformed data.
  
  Returns
  -------
  X_tr : array-like, shape [n_samples, n_features]
      Inverse transformed array.
docstr_tok: ["Convert","the","back","data","to","the","original","representation",".","In","case","unknown","categories","are","encountered","(","all","zeros","in","the","one-hot","encoding",")",",","``","None","``","is","used","to","represent","this","category",".","Parameters","--","--","--","--","--","X",":","array-like","or","sparse","matrix",",","shape","[","n_samples",",","n_encoded_features","]","The","transformed","data",".","Returns","--","--","--","-","X_tr",":","array-like",",","shape","[","n_samples",",","n_features","]","Inverse","transformed","array","."]
code: def inverse_transform(self, X):
      """Convert the back data to the original representation.
  
      In case unknown categories are encountered (all zeros in the
      one-hot encoding), ``None`` is used to represent this category.
  
      Parameters
      ----------
      X : array-like or sparse matrix, shape [n_samples, n_encoded_features]
          The transformed data.
  
      Returns
      -------
      X_tr : array-like, shape [n_samples, n_features]
          Inverse transformed array.
  
      """
      check_is_fitted(self, "categories_")
      X = check_array(X, accept_sparse="csr")
  
      n_samples, _ = X.shape
      n_features = len(self.categories_)
      if self.drop is None:
          n_transformed_features = sum(
              len(cats) for cats in self.categories_
          )
      else:
          n_transformed_features = sum(
              len(cats) - 1 for cats in self.categories_
          )
  
      # validate shape of passed X
      msg = (
          "Shape of the passed X data is not correct. Expected {0} "
          "columns, got {1}."
      )
      if X.shape[1] != n_transformed_features:
          raise ValueError(
              msg.format(n_transformed_features, X.shape[1])
          )
  
      # create resulting array of appropriate dtype
      dt = np.find_common_type(
          [cat.dtype for cat in self.categories_], []
      )
      X_tr = np.empty((n_samples, n_features), dtype=dt)
      j = 0
      found_unknown = {}
  
      for i in range(n_features):
          if self.drop is None:
              cats = self.categories_[i]
          else:
              cats = np.delete(self.categories_[i], self.drop_idx_[i])
          n_categories = len(cats)
  
          # Only happens if there was a column with a unique
          # category. In this case we just fill the column with this
          # unique category value.
          if n_categories == 0:
              X_tr[:, i] = self.categories_[i][self.drop_idx_[i]]
              j += n_categories
              continue
          sub = X[
              :, j : j + n_categories
          ]  # for sparse X argmax returns 2D matrix, ensure 1D array
          labels = np.asarray(sub.argmax(axis=1)).flatten()
          X_tr[:, i] = cats[labels]
          if self.handle_unknown == "ignore":
              unknown = np.asarray(sub.sum(axis=1) == 0).flatten()
              # ignored unknown categories: we have a row of all zero
              if unknown.any():
                  found_unknown[i] = unknown
          # drop will either be None or handle_unknown will be error. If
          # self.drop is not None, then we can safely assume that all of
          # the nulls in each column are the dropped value
          elif self.drop is not None:
              dropped = np.asarray(sub.sum(axis=1) == 0).flatten()
              if dropped.any():
                  X_tr[dropped, i] = self.categories_[i][
                      self.drop_idx_[i]
                  ]
  
          j += n_categories
  
      # if ignored are found: potentially need to upcast result to
      # insert None values
      if found_unknown:
          if X_tr.dtype != object:
              X_tr = X_tr.astype(object)
  
          for idx, mask in found_unknown.items():
              X_tr[mask, idx] = None
  
      return X_tr
  
imports: import copy
  from numbers import Integral
  import numpy
  from scipy import sparse
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from autogluon.features.generators import LabelEncoderFeatureGenerator
label_desc: Use image node_id 7 for calling the OneHotMergeRaresHandleUnknownEncoder obj's underlying member method code with example usage: obj.inverse_transform(X) and returns: X_tr