code: def _get_list_matrix():
      """Returns a list of adjacency matrices, and its sparse and graph versions.
  
      This test-case includes the following corner-cases:
        - single node,
        - multiple nodes,
        - no edges,
        - single edge,
        - and multiple edges.
      """
      adj_matrices = [
          np.array([[2]]),
          np.array([[1, 1, 0], [0, 0, 1], [1, 1, 0]]),
          np.array([[0]]),
          np.array([[]]),
          np.array([[0, 0], [1, 0]]),
      ]
      # Sparse version of the above adjacency matrix.
      sparse_coo_matrices = [
          # (row, column, values, n_node)
          (np.array([0]), np.array([0]), np.array([2]), np.array([1])),
          (
              np.array([0, 0, 1, 2, 2]),
              np.array([0, 1, 2, 0, 1]),
              np.array([1, 1, 1, 1, 1]),
              np.array(3),
          ),
          (np.array([]), np.array([]), np.array([]), np.array(1)),
          (np.array([]), np.array([]), np.array([]), np.array(0)),
          (np.array([1]), np.array([0]), np.array([1]), np.array(2)),
      ]
      expected_graphs = [
          graph.GraphsTuple(
              n_node=jnp.array([1]),
              n_edge=jnp.array([2]),
              nodes=None,
              edges=None,
              globals=None,
              senders=jnp.array([0, 0]),
              receivers=jnp.array([0, 0]),
          ),
          graph.GraphsTuple(
              n_node=jnp.array([3]),
              n_edge=jnp.array([5]),
              nodes=None,
              edges=None,
              globals=None,
              senders=jnp.array([0, 0, 1, 2, 2]),
              receivers=jnp.array([0, 1, 2, 0, 1]),
          ),
          graph.GraphsTuple(
              n_node=jnp.array([1]),
              n_edge=jnp.array([0]),
              nodes=None,
              edges=None,
              globals=None,
              senders=jnp.array([]),
              receivers=jnp.array([]),
          ),
          graph.GraphsTuple(
              n_node=jnp.array([0]),
              n_edge=jnp.array([0]),
              nodes=None,
              edges=None,
              globals=None,
              senders=jnp.array([]),
              receivers=jnp.array([]),
          ),
          graph.GraphsTuple(
              n_node=jnp.array([2]),
              n_edge=jnp.array([1]),
              nodes=None,
              edges=None,
              globals=None,
              senders=jnp.array([1]),
              receivers=jnp.array([0]),
          ),
      ]
      return adj_matrices, sparse_coo_matrices, expected_graphs
  
imports: import functools
  import os
  from absl.testing import absltest
  from absl.testing import parameterized
  import jax
  from jax.lib import xla_bridge
  import jax.numpy
  import jax.tree_util
  from jraph._src import graph
  from jraph._src import utils
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: _get_list_matrix() and returns: adj_matrices,  sparse_coo_matrices,  expected_graphs