code: def test_APPNP_propagate_model_matches_manual(model_type):
      dense_size = 5
  
      G, features = create_graph_features()
      adj = G.to_adjacency_matrix()
      features, adj = GCN_Aadj_feats_op(features, adj)
      adj = np.array(adj.todense()[None, :, :])
      out_indices = np.array([[0, 1]], dtype="int32")
  
      generator = FullBatchNodeGenerator(G, sparse=False, method="gcn")
      appnpnModel = APPNP(
          [dense_size],
          generator=generator,
          activations=["relu"],
          dropout=0.0,
      )
  
      dense = Dense(dense_size)
      if model_type == "sequential":
          fully_connected_model = keras.Sequential()
          fully_connected_model.add(dense)
      else:
          inp = keras.Input(shape=features.shape)
          fully_connected_model = keras.Model(inp, dense(inp))
  
      x_in, x_out = appnpnModel.propagate_model(fully_connected_model)
      end_to_end_model = keras.Model(inputs=x_in, outputs=x_out)
      preds_1 = end_to_end_model.predict(
          [features[None, :, :], out_indices, adj]
      )
  
      # run the process manually: transform the features, and then propagate
      float_feats = features[None, :, :].astype("float32")
      manual_preds = manual_inp = fully_connected_model.predict(
          float_feats
      )
      propagate = APPNPPropagationLayer(
          dense_size, teleport_probability=0.1
      )
      for _ in range(10):
          manual_preds = propagate([manual_preds, manual_inp, adj])
  
      # select the relevant pieces
      manual_preds = manual_preds.numpy()[:, out_indices.ravel(), :]
  
      np.testing.assert_allclose(preds_1, manual_preds)
  
file_path: stellargraph/tests/layer/test_appnp.py
imports: from stellargraph.layer.appnp import *
  from stellargraph.mapper import FullBatchNodeGenerator, FullBatchLinkGenerator
  from stellargraph import StellarGraph
  from stellargraph.core.utils import GCN_Aadj_feats_op
  import networkx
  import pandas
  import numpy
  from tensorflow import keras
  import pytest
  from ..test_utils.graphs import create_graph_features
  from ..None import test_utils
label_desc: Use image node_id 7 for calling a global function with example usage: test_APPNP_propagate_model_matches_manual(model_type) without return types