class_docstr: Tests of GAT class
class_docstr_tok: ["Tests","of","GAT","class"]
code: def test_gat_build_l2norm(self):
      G = example_graph(feature_size=self.F_in)
      gen = FullBatchNodeGenerator(
          G, sparse=self.sparse, method=self.method
      )
      gat = GAT(
          layer_sizes=self.layer_sizes,
          activations=self.activations,
          attn_heads=self.attn_heads,
          generator=gen,
          bias=True,
          normalize="l2",
          kernel_initializer="ones",
          attn_kernel_initializer="ones",
      )
  
      x_in, x_out = gat.in_out_tensors()
  
      model = keras.Model(inputs=x_in, outputs=x_out)
  
      ng = gen.flow(G.nodes())
      actual = model.predict(ng)
      expected = np.ones(
          (G.number_of_nodes(), self.layer_sizes[-1])
      ) * (1.0 / G.number_of_nodes())
  
      np.testing.assert_allclose(expected, actual[0])
  
imports: import pytest
  import scipy.sparse
  import tensorflow
  from tensorflow import keras
  from tensorflow.keras import backend
  from tensorflow.keras.layers import Input
  from stellargraph.mapper import FullBatchNodeGenerator, FullBatchLinkGenerator, GraphSAGENodeGenerator
  from stellargraph.layer import *
  from ..test_utils.graphs import example_graph
  from ..None import test_utils
label_desc: Use image node_id 6 for calling the Test_GAT obj's underlying member method code with example usage: obj.test_gat_build_l2norm() without return types