code: def test_enable_lora(self):
      layer = layers.Dense(units=16)
      layer.build((None, 8))
      layer.enable_lora(4)
      self.assertLen(layer.trainable_weights, 3)
      self.assertLen(layer.non_trainable_weights, 1)
      # Try eager call
      x = np.random.random((64, 8))
      y = np.random.random((64, 16))
      _ = layer(x[:2])
  
      # Try calling fit()
      model = models.Sequential(
          [
              layer,
          ]
      )
      model.compile(optimizer="sgd", loss="mse")
      model.fit(x, y)
  
      # Try saving and reloading the model
      temp_filepath = os.path.join(
          self.get_temp_dir(), "lora_model.keras"
      )
      model.save(temp_filepath)
  
      new_model = saving.load_model(temp_filepath)
      self.assertFalse(new_model.layers[0].lora_enabled)
      self.assertAllClose(model.predict(x), new_model.predict(x))
  
      # Try saving and reloading the model's weights only
      temp_filepath = os.path.join(
          self.get_temp_dir(), "lora_model.weights.h5"
      )
      model.save_weights(temp_filepath)
  
      # Load the file into a fresh, non-lora model
      new_model = models.Sequential(
          [
              layers.Dense(units=16),
          ]
      )
      new_model.build((None, 8))
      new_model.load_weights(temp_filepath)
      self.assertAllClose(model.predict(x), new_model.predict(x))
  
      # Try loading a normal checkpoint into a lora model
      new_model.save_weights(temp_filepath)
      model.load_weights(temp_filepath)
      self.assertAllClose(model.predict(x), new_model.predict(x))
  
imports: import os
  import numpy
  import pytest
  from keras import backend
  from keras import constraints
  from keras import layers
  from keras import models
  from keras import saving
  from keras import testing
  from keras.backend.common import keras_tensor
label_desc: Use image node_id 9 for calling the DenseTest obj's underlying member method code with example usage: obj.test_enable_lora() without return types