code: def test_variable_assignment_reuse_layout(self):
      shape = (4, 2)
      axis_names = ["batch", "model"]
      device_mesh = distribution_lib.DeviceMesh(
          shape, axis_names, backend_dlib.list_devices()
      )
      layout_map = distribution_lib.LayoutMap(device_mesh)
      layout_map[".*dense.*kernel"] = distribution_lib.TensorLayout(
          [None, "model"]
      )
      layout_map[".*dense.*bias"] = distribution_lib.TensorLayout(
          ["model"]
      )
  
      distribution = distribution_lib.ModelParallel(
          device_mesh, layout_map, batch_dim_name="batch"
      )
  
      with distribution.scope():
          dense_layer = layers.Dense(8)
          dense_layer.build((16, 16))
  
      self.assertEqual(
          dense_layer.kernel._value.sharding.spec, (None, "model")
      )
      self.assertEqual(
          dense_layer.bias._value.sharding.spec, ("model",)
      )
  
      # Assign a numpy value to dense layer to mimic the model weight loading
      new_kernel = np.random.normal(size=(16, 8))
      new_bias = np.random.normal(size=(8))
      dense_layer.kernel.assign(new_kernel)
      dense_layer.bias.assign(new_bias)
  
      # Make sure the loaded value still use the layout when it is
      # initialized, even outside of the distribution scope.
      self.assertEqual(
          dense_layer.kernel._value.sharding.spec, (None, "model")
      )
      self.assertEqual(
          dense_layer.bias._value.sharding.spec, ("model",)
      )
  
imports: import functools
  import os
  from unittest import mock
  import jax
  import numpy
  import pytest
  from keras import backend
  from keras import layers
  from keras import models
  from keras import testing
  from keras.backend import distribution_lib
  from keras.distribution import distribution_lib
label_desc: Use image node_id 13 for calling the JaxDistributionLibTest obj's underlying member method code with example usage: obj.test_variable_assignment_reuse_layout() without return types