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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 as_tensor_dict(self, padding_lengths: Dict[str, Dict[str, int]] = None, verbose: bool = False) -> Dict[str, Union[torch.Tensor, Dict[str, torch.Tensor]]]: # This complex return type is actually predefined elsewhere as a DataArray, # but we can't use it because mypy doesn't like it. """ This method converts this ``Batch`` into a set of pytorch Tensors that can be passed through a model. In order for the tensors to be valid tensors, all ``Instances`` in this batch need to be padded to the same lengths wherever padding is necessary, so we do that first, then we combine all of the tensors for each field in each instance into a set of batched tensors for each field. Parameters padding_lengths : ``Dict[str, Dict[str, int]]`` If a key is present in this dictionary with a non-``None`` value, we will pad to that length instead of the length calculated from the data. This lets you, e.g., set a maximum value for sentence length if you want to throw out long sequences. Entries in this dictionary are keyed first by field name (e.g., "question"), then by padding key (e.g., "num_tokens"). verbose : ``bool``, optional (default=``False``) Should we output logging information when we're doing this padding? If the batch is large, this is nice to have, because padding a large batch could take a long time. But if you're doing this inside of a data generator, having all of this output per batch is a bit obnoxious (and really slow). Returns ------- tensors : ``Dict[str, DataArray]`` A dictionary of tensors, keyed by field name, suitable for passing as input to a model. This is a `batch` of instances, so, e.g., if the instances have a "question" field and an "answer" field, the "question" fields for all of the instances will be grouped together into a single tensor, and the "answer" fields for all instances will be similarly grouped in a parallel set of tensors, for batched computation. Additionally, for complex ``Fields``, the value of the dictionary key is not necessarily a single tensor. For example, with the ``TextField``, the output is a dictionary mapping ``TokenIndexer`` keys to tensors. The number of elements in this sub-dictionary therefore corresponds to the number of ``TokenIndexers`` used to index the ``TextField``. Each ``Field`` class is responsible for batching its own output. """

        if padding_lengths is None:
            padding_lengths = defaultdict(dict)
        # First we need to decide _how much_ to pad.  To do that, we find the max length for all
        # relevant padding decisions from the instances themselves.  Then we check whether we were
        # given a max length for a particular field and padding key.  If we were, we use that
        # instead of the instance-based one.
        if verbose:
            logger.info("Padding batch of size %d to lengths %s", len(self.instances), str(padding_lengths))
            logger.info("Getting max lengths from instances")
        instance_padding_lengths = self.get_padding_lengths()
        if verbose:
            logger.info("Instance max lengths: %s", str(instance_padding_lengths))
        lengths_to_use: Dict[str, Dict[str, int]] = defaultdict(dict)
        for field_name, instance_field_lengths in instance_padding_lengths.items():
            for padding_key in instance_field_lengths.keys():
                if padding_lengths[field_name].get(padding_key) is not None:
                    lengths_to_use[field_name][padding_key] = padding_lengths[field_name][padding_key]
                else:
                    lengths_to_use[field_name][padding_key] = instance_field_lengths[padding_key]

        # Now we actually pad the instances to tensors.
        field_tensors: Dict[str, list] = defaultdict(list)
        if verbose:
            logger.info("Now actually padding instances to length: %s", str(lengths_to_use))
        for instance in self.instances:
            for field, tensors in instance.as_tensor_dict(lengths_to_use).items():
                field_tensors[field].append(tensors)

        # Finally, we combine the tensors that we got for each instance into one big tensor (or set
        # of tensors) per field.  The `Field` classes themselves have the logic for batching the
        # tensors together, so we grab a dictionary of field_name -> field class from the first
        # instance in the batch.
        field_classes = self.instances[0].fields
        final_fields = {}
        for field_name, field_tensor_list in field_tensors.items():
            final_fields[field_name] = field_classes[field_name].batch_tensors(field_tensor_list)
        return final_fields