code: def _get_first_padding_graph(
      graphs_batch, padding_size, experimental_unconnected_padding_edges
  ):
      """Gets a dummy graph that pads receivers and senders.
  
      This dummy graph will have number of nodes = padding_size.nodes and
      number of edges = padding_size.edges. Receivers and
      senders will be indexed with all zeros (connecting to the first node in the
      dummy graph).
  
      Args:
        graphs_batch: the `graphs.GraphsTuple` to be padded.
        padding_size: a `GraphsTupleSize` with the padding size.
        experimental_unconnected_padding_edges: see `pad_graphs_tuple` for details.
  
      Returns:
        A `graphs.GraphsTuple` of a single dummy graph.
      """
  
      # Set the edges to belong to an index corresponding to a node that does not
      # exist.
      if experimental_unconnected_padding_edges:
          logging.log_first_n(
              logging.WARNING,
              "Using a padding graph with unconnected edges. This is an experimental "
              "feature which may stop working in the future, and will lead to out"
              "of range errors on tf.scatter if the graph net computation occurs on "
              "CPU.",
              1,
          )
          dummy_senders_and_receivers = (
              tf.ones([padding_size.num_edges], tf.int32)
              * padding_size.num_nodes
          )
      else:
          dummy_senders_and_receivers = tf.zeros(
              [padding_size.num_edges], tf.int32
          )
  
      return graphs.GraphsTuple(
          n_node=[padding_size.num_nodes],
          n_edge=[padding_size.num_edges],
          nodes=tree.map_structure(
              functools.partial(
                  _get_zeros_with_variable_batch_size,
                  padding_size=padding_size.num_nodes,
              ),
              graphs_batch.nodes,
          ),
          edges=tree.map_structure(
              functools.partial(
                  _get_zeros_with_variable_batch_size,
                  padding_size=padding_size.num_edges,
              ),
              graphs_batch.edges,
          ),
          senders=dummy_senders_and_receivers,
          receivers=dummy_senders_and_receivers,
          globals=tree.map_structure(
              functools.partial(
                  _get_zeros_with_variable_batch_size, padding_size=1
              ),
              graphs_batch.globals,
          ),
      )
  
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 39 for calling a global function with example usage: _get_first_padding_graph(graphs_batch, padding_size, experimental_unconnected_padding_edges) and returns: graphs