code: def _concatenate_data_dicts(data_dicts):
      """Concatenate a list of data dicts to create the equivalent batched graph.
  
      Args:
        data_dicts: An iterable of data dictionaries with keys `GRAPH_DATA_FIELDS`,
          plus, potentially, a subset of `GRAPH_NUMBER_FIELDS`. Each dictionary is
          representing a single graph.
  
      Returns:
        A data dictionary with the keys `GRAPH_DATA_FIELDS + GRAPH_NUMBER_FIELDS`,
        representing the concatenated graphs.
      """
      # Create a single dict with fields that contain sequences of graph tensors.
      concatenated_dicts = collections.defaultdict(lambda: [])
      for data_dict in data_dicts:
          data_dict = _populate_number_fields(data_dict)
          for k, v in data_dict.items():
              if v is not None:
                  concatenated_dicts[k].append(v)
              else:
                  concatenated_dicts[k] = None
  
      concatenated_dicts = dict(concatenated_dicts)
  
      for field, arrays in concatenated_dicts.items():
          if arrays is None:
              concatenated_dicts[field] = None
          elif field in list(GRAPH_NUMBER_FIELDS) + [GLOBALS]:
              concatenated_dicts[field] = np.stack(arrays)
          else:
              concatenated_dicts[field] = np.concatenate(arrays, axis=0)
  
      if concatenated_dicts[RECEIVERS] is not None:
          offset = _compute_stacked_offsets(
              concatenated_dicts[N_NODE], concatenated_dicts[N_EDGE]
          )
          for field in (RECEIVERS, SENDERS):
              concatenated_dicts[field] += offset
  
      return concatenated_dicts
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from graph_nets import graphs
  import networkx
  import numpy
  from six.moves import range
  from six.moves import zip
label_desc: Use image node_id 15 for calling a global function with example usage: _concatenate_data_dicts(data_dicts) and returns: concatenated_dicts