(self)
Generator that can be used to iterate over this dataset.

        Yields:
            tuple : the next minibatch of data.
Generator that can be used to iterate over this dataset.
Generator that can be used to iterate over this dataset .
def __iter__(self):
        """
        Generator that can be used to iterate over this dataset.

        Yields:
            tuple : the next minibatch of data.
        """
        self.batch_index = 0
        shuf_idx = self.be.rng.permutation(len(self.story))
        self.story = self.story[shuf_idx]
        self.query = self.query[shuf_idx]
        self.answer = self.answer[shuf_idx]

        while self.batch_index < self.nbatches:
            batch = slice(self.batch_index * self.be.bsz, (self.batch_index + 1) * self.be.bsz)
            story_tensor = self.be.array(self.story[batch].T.copy())
            query_tensor = self.be.array(self.query[batch].T.copy())
            answer_tensor = self.be.array(self.answer[batch].T.copy())

            self.batch_index += 1

            yield (story_tensor, query_tensor), answer_tensor
def __iter__ ( self ) : self . batch_index = 0 shuf_idx = self . be . rng . permutation ( len ( self . story ) ) self . story = self . story [ shuf_idx ] self . query = self . query [ shuf_idx ] self . answer = self . answer [ shuf_idx ] while self . batch_index < self . nbatches : batch = slice ( self . batch_index * self . be . bsz , ( self . batch_index + 1 ) * self . be . bsz ) story_tensor = self . be . array ( self . story [ batch ] . T . copy ( ) ) query_tensor = self . be . array ( self . query [ batch ] . T . copy ( ) ) answer_tensor = self . be . array ( self . answer [ batch ] . T . copy ( ) ) self . batch_index += 1 yield ( story_tensor , query_tensor ) , answer_tensor