      # test get_weights , set_weights at layer level
      weights = layer.get_weights()
      layer.set_weights(weights)
  
      if isinstance(layer, (layers.ReflectionPadding2D, normalization.InstanceNormalization)):
          layer.build(input_shape)
      expected_output_shape = layer.compute_output_shape(input_shape)
  
      # test in functional API
      if fixed_batch_size:
          inp = Input(batch_shape=input_shape, dtype=input_dtype)
 
</s> remove from lib.model import layers, normalization
 </s> add from lib.model import layers
    layer.build(input_shape)
      # test get_weights , set_weights at layer level
      weights = layer.get_weights()
      layer.set_weights(weights)
  
    layer.build(input_shape)
    layer.build(input_shape)
      expected_output_shape = layer.compute_output_shape(input_shape)
  
      # test in functional API
      if fixed_batch_size:
          inp = Input(batch_shape=input_shape, dtype=input_dtype)
keep keep keep keep replace replace keep keep keep keep keep
 lib.model.normalization:
  - refactor
  - add LayerNorm
  - add RMSNorm 
tests/lib/model/layers_test.py