code: def test_conv1d_update_state(self):
      batch = 2
      d_model = 6
      filter_size = 3
      batch_dim = mtf.Dimension("batch", batch)
      filter_dim = mtf.Dimension("filter", filter_size)
  
      x = np.random.randn(batch, d_model)
      x_mtf = self.converter.convert_np_array_to_mtf_tensor(
          x, dtype=tf.float32, dim_names=["batch", "d_model"]
      )
  
      old_state = np.random.randn(batch, filter_size, d_model)
      old_state_mtf = self.converter.convert_np_array_to_mtf_tensor(
          old_state,
          dtype=tf.float32,
          dim_names=["batch", "filter", "d_model"],
      )
  
      position_mtf = mtf.constant(
          self.converter.mesh,
          filter_size - 1,
          shape=mtf.Shape([batch_dim]),
          dtype=tf.int32,
      )
      conv_layer = transformer_layers.Conv1D()
      output_mtf = conv_layer.update_state(
          old_state_mtf,
          x_mtf,
          position_mtf,
          filter_dim,
          dtype=tf.float32,
      )
      actual = self.converter.convert_mtf_tensor_to_np_array(output_mtf)
  
      expected = np.empty(shape=old_state.shape)
      expected[:, : filter_size - 1, :] = old_state[:, 1:, :]
      expected[:, -1, :] = x
      self.assertAllClose(actual, expected)
  
imports: import collections
  import mesh_tensorflow
  from mesh_tensorflow import test_utils
  from mesh_tensorflow.transformer import transformer
  from mesh_tensorflow.transformer import transformer_layers
  import mock
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 6 for calling the TransformerLayersTest obj's underlying member method code with example usage: obj.test_conv1d_update_state() without return types