code: def test_layer_stack_call_padding_handling(self):
      self.converter = test_utils.NumpyConverter()
      x = np.random.randn(2, 3, 4, 5)
      layer_stack, context = create_dummy_model(
          self.converter.mesh, shapes=x.shape
      )
  
      # The last two sequence positions are padding.
      x[:, :, -2:, :] *= 0
      sequence_id = np.ones_like(x, dtype=np.int32)
      sequence_id[:, :, -2:, :] *= 0
      context.sequence_id = (
          self.converter.convert_np_array_to_mtf_tensor(
              sequence_id, dim_names=self.default_dim_names
          )
      )
  
      x_mtf = self.converter.convert_np_array_to_mtf_tensor(
          x, dim_names=self.default_dim_names, dtype=tf.float32
      )
      output_mtf = layer_stack.call(context, x_mtf)
      # [2, 3, 4, 5] -> [2, 3, 2, 5]
      actual = self.converter.convert_mtf_tensor_to_np_array(output_mtf)
  
      # After pooling, the last sequence position should be padding, i.e., zeros.
      last_position = actual[:, :, -1, :]
      self.assertAllClose(last_position, np.zeros_like(last_position))
  
file_path: mesh/mesh_tensorflow/transformer/funnel_transformer_test.py
imports: from absl.testing import parameterized
  import mesh_tensorflow
  from mesh_tensorflow import test_utils
  from mesh_tensorflow.transformer import funnel_transformer
  from mesh_tensorflow.transformer import transformer
  from mesh_tensorflow.transformer import transformer_layers
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the FunnelTransformerTest obj's underlying member method code with example usage: obj.test_layer_stack_call_padding_handling() without return types