code: def create_GAT_model(graph):
      generator = FullBatchNodeGenerator(
          graph, sparse=False, method=None
      )
      train_gen = generator.flow([0, 1], np.array([[1, 0], [0, 1]]))
  
      gat = GAT(
          layer_sizes=[2, 2],
          generator=generator,
          bias=False,
          in_dropout=0,
          attn_dropout=0,
          activations=["elu", "softmax"],
          normalize=None,
          saliency_map_support=True,
      )
      for layer in gat._layers:
          layer._initializer = "ones"
      x_inp, x_out = gat.in_out_tensors()
      keras_model = Model(inputs=x_inp, outputs=x_out)
      return gat, keras_model, generator, train_gen
  
file_path: stellargraph/tests/interpretability/test_saliency_maps_gat.py
imports: import pytest
  from stellargraph.interpretability.saliency_maps import IntegratedGradientsGAT
  import numpy
  from stellargraph.layer import GAT
  from stellargraph.mapper import FullBatchNodeGenerator
  from tensorflow.keras import Model
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.losses import categorical_crossentropy
  import networkx
  from tensorflow.keras import backend
  from ..test_utils.graphs import example_graph_1_saliency_maps
label_desc: Use image node_id 1 for calling a global function with example usage: create_GAT_model(graph) and returns: gat,  keras_model,  generator,  train_gen