code: def networkxs_to_graphs_tuple(
      graph_nxs,
      node_shape_hint=None,
      edge_shape_hint=None,
      data_type_hint=np.float32,
  ):
      """Constructs an instance from an iterable of networkx 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`.
  
      The output data is a sequence of data dicts with fields:
        NODES, EDGES, RECEIVERS, SENDERS, GLOBALS, N_NODE, N_EDGE.
  
      Args:
        graph_nxs: A container of `networkx.OrderedMultiDiGraph`s. The node keys
          must be sequential integer values following the order in which nodes are
          added to the graph starting from zero. That is
          `list(graph_nx.nodes)[i] == i`.
        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.
  
      Returns:
        The instance.
  
      Raises:
        ValueError: If `graph_nxs` is not an iterable of networkx instances.
      """
      data_dicts = []
      try:
          for graph_nx in graph_nxs:
              data_dict = networkx_to_data_dict(
                  graph_nx,
                  node_shape_hint,
                  edge_shape_hint,
                  data_type_hint,
              )
              data_dicts.append(data_dict)
      except TypeError as e:
          raise ValueError(
              "Could not convert some elements of `graph_nxs`. "
              "Did you pass an iterable of networkx instances?"
          ) from e
  
      return data_dicts_to_graphs_tuple(data_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 9 for calling a global function with example usage: networkxs_to_graphs_tuple(graph_nxs, node_shape_hint, edge_shape_hint, data_type_hint) and returns: data_dicts_to_graphs_tuple