code: def pad_with_graphs(
      graph: gn_graph.GraphsTuple,
      n_node: int,
      n_edge: int,
      n_graph: int = 2,
  ) -> gn_graph.GraphsTuple:
      """Pads a ``GraphsTuple`` to size by adding computation preserving graphs.
  
      The ``GraphsTuple`` is padded by first adding a dummy graph which contains the
      padding nodes and edges, and then empty graphs without nodes or edges.
  
      The empty graphs and the dummy graph do not interfer with the graphnet
      calculations on the original graph, and so are computation preserving.
  
      The padding graph requires at least one node and one graph.
  
      This function does not support jax.jit, because the shape of the output
      is data-dependent.
  
      Args:
        graph: ``GraphsTuple`` padded with dummy graph and empty graphs.
        n_node: the number of nodes in the padded ``GraphsTuple``.
        n_edge: the number of edges in the padded ``GraphsTuple``.
        n_graph: the number of graphs in the padded ``GraphsTuple``. Default is 2,
          which is the lowest possible value, because we always have at least one
          graph in the original ``GraphsTuple`` and we need one dummy graph for the
          padding.
  
      Raises:
        ValueError: if the passed ``n_graph`` is smaller than 2.
        RuntimeError: if the given ``GraphsTuple`` is too large for the given
          padding.
  
      Returns:
        A padded ``GraphsTuple``.
      """
      if n_graph < 2:
          raise ValueError(
              f"n_graph is {n_graph}, which is smaller than minimum value of 2."
          )
      graph = jax.device_get(graph)
      pad_n_node = int(n_node - np.sum(graph.n_node))
      pad_n_edge = int(n_edge - np.sum(graph.n_edge))
      pad_n_graph = int(n_graph - graph.n_node.shape[0])
      if pad_n_node <= 0 or pad_n_edge < 0 or pad_n_graph <= 0:
          raise RuntimeError(
              "Given graph is too large for the given padding. difference: "
              f"n_node {pad_n_node}, n_edge {pad_n_edge}, n_graph {pad_n_graph}"
          )
  
      pad_n_empty_graph = pad_n_graph - 1
  
      tree_nodes_pad = lambda leaf: np.zeros(
          (pad_n_node,) + leaf.shape[1:], dtype=leaf.dtype
      )
      tree_edges_pad = lambda leaf: np.zeros(
          (pad_n_edge,) + leaf.shape[1:], dtype=leaf.dtype
      )
      tree_globs_pad = lambda leaf: np.zeros(
          (pad_n_graph,) + leaf.shape[1:], dtype=leaf.dtype
      )
  
      padding_graph = gn_graph.GraphsTuple(
          n_node=np.concatenate(
              [
                  np.array([pad_n_node], dtype=np.int32),
                  np.zeros(pad_n_empty_graph, dtype=np.int32),
              ]
          ),
          n_edge=np.concatenate(
              [
                  np.array([pad_n_edge], dtype=np.int32),
                  np.zeros(pad_n_empty_graph, dtype=np.int32),
              ]
          ),
          nodes=tree.tree_map(tree_nodes_pad, graph.nodes),
          edges=tree.tree_map(tree_edges_pad, graph.edges),
          globals=tree.tree_map(tree_globs_pad, graph.globals),
          senders=np.zeros(pad_n_edge, dtype=np.int32),
          receivers=np.zeros(pad_n_edge, dtype=np.int32),
      )
      return _batch([graph, padding_graph], np_=np)
  
imports: import functools
  from typing import Any, Callable, Generator, Iterable, Iterator, List, Mapping, Optional, Sequence, Union
  import jax
  from jax import lax
  import jax.numpy
  import jax.tree_util
  from jraph._src import graph
  import numpy
label_desc: Use image node_id 18 for calling a global function with example usage: pad_with_graphs(graph, n_node, n_edge, n_graph) and returns: _batch