code: def test_prelu_correctness(self):
      def np_prelu(x, alpha):
          return (x > 0) * x + (x <= 0) * alpha * x
  
      inputs = np.random.randn(2, 10, 5, 3)
      prelu_layer = prelu.PReLU(
          alpha_initializer="glorot_uniform",
          alpha_regularizer="l1",
          alpha_constraint="non_neg",
          shared_axes=(1, 2),
      )
      prelu_layer.build(inputs.shape)
  
      weights = np.random.random((1, 1, 3))
      prelu_layer.alpha.assign(weights)
      ref_out = np_prelu(inputs, weights)
      self.assertAllClose(prelu_layer(inputs), ref_out)
  
file_path: keras/keras/layers/activations/prelu_test.py
imports: import numpy
  import pytest
  from keras import testing
  from keras.layers.activations import prelu
label_desc: Use image node_id 2 for calling the PReLUTest obj's underlying member method code with example usage: obj.test_prelu_correctness() and returns: unknown