code: def placeholders_from_networkxs(
      graph_nxs,
      node_shape_hint=None,
      edge_shape_hint=None,
      data_type_hint=tf.float32,
      force_dynamic_num_graphs=True,
      name="placeholders_from_networkxs",
  ):
      """Constructs placeholders compatible with a list of networkx instances.
  
      Given a list of networkxs instances, constructs placeholders compatible with
      the shape of those graphs.
  
      The networkx graph should be set up such that, for fixed shapes `node_shape`,
       `edge_shape` and `global_shape`:
        - `graph_nx.nodes(data=True)[i][-1]["features"]` is, for any node index i, a
          tensor of shape `node_shape`, or `None`;
        - `graph_nx.edges(data=True)[i][-1]["features"]` is, for any edge index i, a
          tensor of shape `edge_shape`, or `None`;
        - `graph_nx.edges(data=True)[i][-1]["index"]`, if present, defines the order
          in which the edges will be sorted in the resulting `data_dict`;
        - `graph_nx.graph["features"] is a tensor of shape `global_shape` or `None`.
  
      Args:
        graph_nxs: A container of `networkx.MultiDiGraph`s.
        node_shape_hint: (iterable of `int` or `None`, default=`None`) If the graph
          does not contain nodes, the trailing shape for the created `NODES` field.
          If `None` (the default), this field is left `None`. This is not used if
          `graph_nx` contains at least one node.
        edge_shape_hint: (iterable of `int` or `None`, default=`None`) If the graph
          does not contain edges, the trailing shape for the created `EDGES` field.
          If `None` (the default), this field is left `None`. This is not used if
          `graph_nx` contains at least one edge.
        data_type_hint: (numpy dtype, default=`np.float32`) If the `NODES` or
          `EDGES` fields are autocompleted, their type.
        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 graph_nxs.
      """
      with tf.name_scope(name):
          graph = utils_np.networkxs_to_graphs_tuple(
              graph_nxs,
              node_shape_hint,
              edge_shape_hint,
              data_type_hint.as_numpy_dtype,
          )
          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 6 for calling a global function with example usage: placeholders_from_networkxs(graph_nxs, node_shape_hint, edge_shape_hint, data_type_hint, force_dynamic_num_graphs, name) and returns: _placeholders_from_graphs_tuple