code: def test_GCN_apply_dense():
      G, features = create_graph_features()
      adj = G.to_adjacency_matrix().toarray()[None, :, :]
      n_nodes = features.shape[0]
  
      generator = FullBatchNodeGenerator(G, sparse=False, method="none")
      gcnModel = GCN([2], generator, activations=["relu"], dropout=0.5)
  
      x_in, x_out = gcnModel.in_out_tensors()
      model = keras.Model(inputs=x_in, outputs=x_out)
  
      # Check fit method
      out_indices = np.array([[0, 1]], dtype="int32")
      preds_1 = model.predict([features[None, :, :], out_indices, adj])
      assert preds_1.shape == (1, 2, 2)
  
      # Check fit method
      preds_2 = model.predict(generator.flow(["a", "b"]))
      assert preds_2.shape == (1, 2, 2)
  
      assert preds_1 == pytest.approx(preds_2)
  
imports: import stellargraph
  from stellargraph.layer.gcn import *
  from stellargraph.mapper import FullBatchNodeGenerator, FullBatchLinkGenerator
  from stellargraph.core.graph import StellarGraph
  from stellargraph.core.utils import GCN_Aadj_feats_op
  import networkx
  import pandas
  import numpy
  from tensorflow import keras
  import tensorflow
  import pytest
  from ..test_utils.graphs import create_graph_features
  from ..None import test_utils
label_desc: Use image node_id 6 for calling a global function with example usage: test_GCN_apply_dense() without return types