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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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