code: def test_feather_node():
      """
      Testing the FEATHER node embedding.
      """
      graph = nx.newman_watts_strogatz_graph(250, 10, 0.2)
  
      features = np.random.uniform(0, 1, (250, 2000))
      model = FeatherNode()
      model.fit(graph, features)
      embedding = model.get_embedding()
  
      assert embedding.shape[0] == graph.number_of_nodes()
      assert (
          embedding.shape[1]
          == 2
          * model.order
          * model.eval_points
          * model.reduction_dimensions
      )
      assert type(embedding) == np.ndarray
  
      features = np.random.uniform(0, 1, (250, 16))
      model = FeatherNode()
      model.fit(graph, features)
      embedding = model.get_embedding()
  
      assert embedding.shape[0] == graph.number_of_nodes()
      assert (
          embedding.shape[1]
          == 2 * model.order * model.eval_points * features.shape[1]
      )
      assert type(embedding) == np.ndarray
  
      features = {i: random.sample(range(150), 50) for i in range(250)}
  
      row = np.array([k for k, v in features.items() for val in v])
      col = np.array([val for k, v in features.items() for val in v])
      data = np.ones(250 * 50)
      shape = (250, 150)
  
      features = coo_matrix((data, (row, col)), shape=shape)
      model = FeatherNode()
      model.fit(graph, features)
      embedding = model.get_embedding()
  
      assert embedding.shape[0] == graph.number_of_nodes()
      assert (
          embedding.shape[1]
          == 2
          * model.order
          * model.eval_points
          * model.reduction_dimensions
      )
      assert type(embedding) == np.ndarray
  
file_path: karateclub/test/attributed_node_embedding_test.py
imports: import random
  import numpy
  import networkx
  from scipy.sparse import coo_matrix
  from karateclub.node_embedding.attributed import BANE, TENE, TADW, FSCNMF, SINE, MUSAE, FeatherNode, ASNE, AE
label_desc: Use image node_id 2 for calling a global function with example usage: test_feather_node() without return types