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: Transform X using one-hot encoding.
  
  Parameters
  ----------
  X : array-like, shape [n_samples, n_features]
      The data to encode.
  
  Returns
  -------
  X_out : sparse matrix if sparse=True else a 2-d array
      Transformed input.
docstr_tok: ["Transform","X","using","one-hot","encoding",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","[","n_samples",",","n_features","]","The","data","to","encode",".","Returns","--","--","--","-","X_out",":","sparse","matrix","if","sparse=True","else","a","2-d","array","Transformed","input","."]
code: def transform(self, X):
      """Transform X using one-hot encoding.
  
      Parameters
      ----------
      X : array-like, shape [n_samples, n_features]
          The data to encode.
  
      Returns
      -------
      X_out : sparse matrix if sparse=True else a 2-d array
          Transformed input.
      """
      X = self._convert_cat_to_int(X=X)
      X = np.array(
          X
      ).tolist()  # converts all elements in X to the same type (i.e. cannot mix floats, ints, and str)
      check_is_fitted(self, "categories_")
      # validation of X happens in _check_X called by _transform
      X_int, X_mask = self._transform(
          X, handle_unknown=self.handle_unknown
      )
      n_samples, n_features = X_int.shape
  
      # n_columns indicates, for each feature, how many columns are used in
      # X_trans. By default this corresponds to the number of categories, but
      # will differ if we drop some of them, or if there are infrequent
      # categories (all mapped to the same column)
      n_columns = [len(cats) for cats in self.categories_]
      for feature_idx in range(n_features):
          n_infrequent = self.infrequent_indices_[feature_idx].size
          if n_infrequent > 0:
              # still add 1 for the infrequent column
              n_columns[feature_idx] += 1 - n_infrequent
          if self.drop is not None:
              # if drop is not None we always drop one column in general,
              # except when drop is 'infrequent' and there is no infrequent
              # category.
              n_columns[feature_idx] -= 1
              if (
                  isinstance(self.drop, str)
                  and self.drop == "infrequent"
                  and n_infrequent == 0
              ):
                  n_columns[
                      feature_idx
                  ] += 1  # revert decrement from above
  
      if self.drop is not None:
          to_drop = self.drop_idx_.copy()
          if isinstance(self.drop, str):
              if self.drop == "infrequent":
                  for feature_idx in range(n_features):
                      if self.infrequent_indices_[feature_idx].size > 0:
                          # drop the infrequent column (i.e. the last one)
                          to_drop[feature_idx] = n_columns[feature_idx]
                      else:
                          # no infrequent category, use special marker -1
                          # so that no dropping happens for this feature
                          to_drop[feature_idx] = -1
          else:
              # self.drop is an array of categories. we need to remap the
              # dropped indexes if some of the categories are infrequent.
              # see _transform() for details about the mapping.
              for feature_idx in range(n_features):
                  if self.infrequent_indices_[feature_idx].size > 0:
                      mapping = self._infrequent_mappings[feature_idx]
                      to_drop[feature_idx] = mapping[
                          to_drop[feature_idx]
                      ]
  
          # We remove all the dropped categories from mask, and decrement
          # all categories that occur after them to avoid an empty column.
          to_drop = to_drop.reshape(1, -1)
          keep_cells = (X_int != to_drop) | (to_drop == -1)
          X_mask &= keep_cells
          X_int[(X_int > to_drop) & (to_drop != -1)] -= 1
  
      mask = X_mask.ravel()
      n_values = np.array([0] + n_columns)
      feature_indices = np.cumsum(n_values)
      indices = (X_int + feature_indices[:-1]).ravel()[mask]
      indptr = X_mask.sum(axis=1).cumsum()
      indptr = np.insert(indptr, 0, 0)
      data = np.ones(n_samples * n_features)[mask]
  
      out = sparse.csr_matrix(
          (data, indices, indptr),
          shape=(n_samples, feature_indices[-1]),
          dtype=self.dtype,
      )
      if not self.sparse:
          return out.toarray()
      else:
          return out
  
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 6 for calling the OneHotMergeRaresHandleUnknownEncoder obj's underlying member method code with example usage: obj.transform(X) and returns: out, out