code: def test_meanpool_agg_apply_no_bias():
      # By default, bias_initializers="zeros", so all bias terms are initialised to zeros.
      agg = MeanPoolingAggregator(
          2, act="linear", kernel_initializer="ones"
      )
      assert (
          agg.get_config()["kernel_initializer"]["class_name"] == "Ones"
      )
      assert (
          agg.get_config()["bias_initializer"]["class_name"] == "Zeros"
      )
  
      # Self features
      inp1 = keras.Input(shape=(1, 2))
      # Neighbour features
      inp2 = keras.Input(shape=(1, 2, 2))
  
      out = agg([inp1, inp2])
  
      # Check sizes
      assert agg.weight_dims == [1, 1]
  
      # Numerical test values
      x1 = np.array([[[1, 1]]])
      x2 = np.array([[[[2, 2], [3, 3]]]])
  
      # Agg output:
      # neigh_agg = mean(relu(x2 · ones(2x2) + zeros(2)), axis=1)
      #   = mean([[4,4],[6,6]]) = [[5,5]]
      # from_self = K.dot(x1, ones) = [[2]]
      # from_neigh = K.dot(neigh_agg, ones) = [[10]]
  
      model = keras.Model(inputs=[inp1, inp2], outputs=out)
      actual = model.predict([x1, x2])
      expected = np.array([[[2, 10]]])
  
      assert expected == pytest.approx(actual)
  
imports: from tensorflow import keras
  from tensorflow.keras import initializers, regularizers
  import tensorflow
  import numpy
  import pytest
  from stellargraph.mapper import GraphSAGENodeGenerator
  from stellargraph.layer.graphsage import GraphSAGE, MeanAggregator, MaxPoolingAggregator, MeanPoolingAggregator, AttentionalAggregator
  from ..test_utils.graphs import example_graph
  from ..None import test_utils
label_desc: Use image node_id 14 for calling a global function with example usage: test_meanpool_agg_apply_no_bias() without return types