code: def collate_fn(cls, batch):
      # cls.collate_fn
      elem = batch[0]
      if isinstance(elem, Mapping):  # dict
          assert set(elem.keys()) == {"data_row", "metadata"}
          data_rows = [x["data_row"] for x in batch]
          metadata = [x["metadata"] for x in batch]
          collated_data_rows = {
              key: flat_collate_fn([getattr(d, key) for d in data_rows])
              for key in data_rows[0].to_dict()
          }
          collated_metadata = metadata_collate_fn(metadata)
          combined = combine_dicts(
              [collated_data_rows, collated_metadata]
          )
          batch_dict = {}
          for field, field_type in cls.Batch.get_annotations().items():
              batch_dict[field] = combined.pop(field)
              if field_type == torch.FloatTensor:
                  # Ensure that floats stay as float32
                  batch_dict[field] = batch_dict[field].float()
          out_batch = cls.Batch(**batch_dict)
          remainder = combined
          return out_batch, remainder
      else:
          raise TypeError(f"Unknown type for collate_fn {type(elem)}")
  
imports: import numpy
  from typing import NamedTuple, Mapping, Dict
  from enum import Enum
  from dataclasses import dataclass
  import torch
  import torch.utils.data.dataloader
  from jiant.utils.python.datastructures import ExtendedDataClassMixin, combine_dicts
label_desc: Use image node_id 5 for calling the Task obj's underlying member method code with example usage: obj.collate_fn(cls, batch) and returns: out_batch,  remainder