def __call__(self, corpus):
return _EmbeddingCenterContextBatchify(<EOL>corpus, self._batch_size, self._window_size,<EOL>self._reduce_window_size_randomly, self._shuffle, cbow=self._cbow,<EOL>weight_dtype=self._weight_dtype, index_dtype=self._index_dtype)<EOL>
Batchify a dataset.

        Parameters
        ----------
        corpus : list of sentences
            List of sentences. Any list containing for example integers or
            strings can be a sentence. Context samples do not cross sentence
            boundaries.

         Returns
         -------
         DataStream
             Each element of the DataStream is a tuple of 2 elements (center,
             context). center is a numpy.ndarray of shape (batch_size, ).
             context is a tuple of 3 numpy.ndarray, representing a sparse COO
             array (data, row, col). The center and context arrays contain the
             center and corresponding context words respectively. A sparse
             representation is used for context as the number of context words
             for one center word varies based on the randomly chosen context
             window size and sentence boundaries. The returned center and col
             arrays are of the same dtype as the sentence elements.