code: def placeholders_from_data_dicts(
      data_dicts,
      force_dynamic_num_graphs=True,
      name="placeholders_from_data_dicts",
  ):
      """Constructs placeholders compatible with a list of data dicts.
  
      Args:
        data_dicts: An iterable of data dicts containing numpy arrays.
        force_dynamic_num_graphs: A `bool` that forces the batch dimension to be
          dynamic. Defaults to `True`.
        name: (string, optional) A name for the operation.
  
      Returns:
        An instance of `graphs.GraphTuple` placeholders compatible with the
          dimensions of the dictionaries in `data_dicts`.
      """
      with tf.name_scope(name):
          graph = data_dicts_to_graphs_tuple(data_dicts)
          return _placeholders_from_graphs_tuple(
              graph, force_dynamic_num_graphs=force_dynamic_num_graphs
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import functools
  from absl import logging
  from graph_nets import graphs
  from graph_nets import utils_np
  import six
  from six.moves import range
  import tensorflow
  import tree
label_desc: Use image node_id 5 for calling a global function with example usage: placeholders_from_data_dicts(data_dicts, force_dynamic_num_graphs, name) and returns: _placeholders_from_graphs_tuple