class_docstr: Encodes any arbitrary string as a one-hot array.
  
  This featurizer uses the sklearn OneHotEncoder to create
  sparse matrix representation of a one-hot array of any string.
  It is expected to be used in large datasets that produces memory overload
  using standard featurizer such as OneHotFeaturizer. For example: SwissprotDataset
  
  
  Examples
  --------
  >>> import deepchem as dc
  >>> featurizer = dc.feat.SparseMatrixOneHotFeaturizer()
  >>> sequence = "MMMQLA"
  >>> encodings = featurizer.featurize([sequence])
  >>> encodings[0].shape
  (6, 25)
docstr: Convert from one hot representation back to original string
  
  Parameters
  ----------
  one_hot_vectors: np.ndarray
      An array of one hot encoded features.
  
  Returns
  -------
  str
      Original string for an one hot encoded array.
code: def untransform(
      self, one_hot_vectors: scipy.sparse.base.spmatrix
  ) -> str:
      """Convert from one hot representation back to original string
  
      Parameters
      ----------
      one_hot_vectors: np.ndarray
          An array of one hot encoded features.
  
      Returns
      -------
      str
          Original string for an one hot encoded array.
  
      """
      string = ""
      invers_trans = self.ohe.inverse_transform(one_hot_vectors)
      for one_hot in invers_trans:
          string += one_hot[0]
      return string
  
file_path: deepchem/deepchem/feat/molecule_featurizers/sparse_matrix_one_hot_featurizer.py
imports: import logging
  from typing import List
  import numpy
  import scipy
  from deepchem.feat.base_classes import Featurizer
  from typing import Any, Iterable
label_desc: Use image node_id 4 for calling the SparseMatrixOneHotFeaturizer obj's underlying member method code with example usage: obj.untransform(one_hot_vectors) and returns: string