code: def get_padding_lengths(self) -> Dict[str, Dict[str, int]]:
          
          padding_lengths: Dict[str, Dict[str, int]] = defaultdict(dict)
          all_instance_lengths: List[Dict[str, Dict[str, int]]] = [instance.get_padding_lengths()
                                                                   for instance in self.instances]
          if not all_instance_lengths:
              return {**padding_lengths}
          all_field_lengths: Dict[str, List[Dict[str, int]]] = defaultdict(list)
          for instance_lengths in all_instance_lengths:
              for field_name, instance_field_lengths in instance_lengths.items():
                  all_field_lengths[field_name].append(instance_field_lengths)
          for field_name, field_lengths in all_field_lengths.items():
              for padding_key in field_lengths[0].keys():
                  max_value = max(x[padding_key] if padding_key in x else 0 for x in field_lengths)
                  padding_lengths[field_name][padding_key] = max_value
          return {**padding_lengths}
docs: Gets the maximum padding lengths from all ``Instances`` in this batch.  Each ``Instance``
          has multiple ``Fields``, and each ``Field`` could have multiple things that need padding.
          We look at all fields in all instances, and find the max values for each (field_name,
          padding_key) pair, returning them in a dictionary.
  
          This can then be used to convert this batch into arrays of consistent length, or to set
          model parameters, etc.