Convert a padded minibatch into a variable tensor.

        Each item in the minibatch will be numericalized independently and the resulting
        tensors will be stacked at the first dimension.

        Arguments:
            arr (List[List[str]]): List of tokenized and padded examples.
            device (str or torch.device): A string or instance of `torch.device`
                specifying which device the Variables are going to be created on.
                If left as default, the tensors will be created on cpu. Default: None.

def numericalize(self, arrs, device=None):
        """Convert a padded minibatch into a variable tensor.

        Each item in the minibatch will be numericalized independently and the resulting
        tensors will be stacked at the first dimension.

        Arguments:
            arr (List[List[str]]): List of tokenized and padded examples.
            device (str or torch.device): A string or instance of `torch.device`
                specifying which device the Variables are going to be created on.
                If left as default, the tensors will be created on cpu. Default: None.
        """
        numericalized = []
        self.nesting_field.include_lengths = False
        if self.include_lengths:
            arrs, sentence_lengths, word_lengths = arrs

        for arr in arrs:
            numericalized_ex = self.nesting_field.numericalize(
                arr, device=device)
            numericalized.append(numericalized_ex)
        padded_batch = torch.stack(numericalized)

        self.nesting_field.include_lengths = True
        if self.include_lengths:
            sentence_lengths = \
                torch.tensor(sentence_lengths, dtype=self.dtype, device=device)
            word_lengths = torch.tensor(word_lengths, dtype=self.dtype, device=device)
            return (padded_batch, sentence_lengths, word_lengths)
        return padded_batch