code: def test_ln_basics(self):
      self.run_layer_test(
          layers.LayerNormalization,
          init_kwargs={
              "gamma_regularizer": regularizers.L2(0.01),
              "beta_regularizer": regularizers.L2(0.01),
          },
          input_shape=(3, 4, 2),
          expected_output_shape=(3, 4, 2),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          expected_num_losses=2,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.LayerNormalization,
          init_kwargs={
              "gamma_initializer": "ones",
              "beta_initializer": "ones",
          },
          input_shape=(3, 4, 2),
          expected_output_shape=(3, 4, 2),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          expected_num_losses=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.LayerNormalization,
          init_kwargs={"scale": False, "center": False},
          input_shape=(3, 3),
          expected_output_shape=(3, 3),
          expected_num_trainable_weights=0,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          expected_num_losses=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.LayerNormalization,
          init_kwargs={"rms_scaling": True},
          input_shape=(3, 3),
          expected_output_shape=(3, 3),
          expected_num_trainable_weights=1,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          expected_num_losses=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.LayerNormalization,
          init_kwargs={"axis": (-3, -2, -1)},
          input_shape=(2, 8, 8, 3),
          expected_output_shape=(2, 8, 8, 3),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          expected_num_losses=0,
          supports_masking=True,
      )
      self.run_layer_test(
          layers.LayerNormalization,
          init_kwargs={},
          input_shape=(1, 0, 10),
          expected_output_shape=(1, 0, 10),
          expected_num_trainable_weights=2,
          expected_num_non_trainable_weights=0,
          expected_num_seed_generators=0,
          expected_num_losses=0,
          supports_masking=True,
      )
  
file_path: keras/keras/layers/normalization/layer_normalization_test.py
imports: import numpy
  import pytest
  from keras import layers
  from keras import ops
  from keras import regularizers
  from keras import testing
label_desc: Use image node_id 1 for calling the LayerNormalizationTest obj's underlying member method code with example usage: obj.test_ln_basics() without return types