code: def test_correctness(self):
      query = np.array([[[1.0, 0.0], [0.0, 1.0]]])
      key = np.array([[[0.0, 1.0], [1.0, 0.0]]])
      value = np.array([[[1.0, 2.0], [3.0, 4.0]]])
  
      # Setup layer.
      num_heads = 2
      key_dim = 2
      layer = layers.MultiHeadAttention(
          num_heads=num_heads,
          key_dim=key_dim,
      )
      layer.build(query.shape, key.shape, value.shape)
  
      # Set layer weights.
      kernel = np.identity(key_dim)
      # To get an identity kernel we need to add a head dim and repeat on it.
      kernel = np.repeat(kernel[:, np.newaxis, :], num_heads, axis=1)
      # Zeros for all biases.
      bias = np.zeros((2, 2))
      output_bias = np.zeros((2,))
      layer.set_weights([kernel, bias] * 3 + [kernel, output_bias])
  
      # Call layer and assert output.
      output, scores = layer(
          query=query,
          value=value,
          key=key,
          return_attention_scores=True,
      )
      self.assertAllClose(
          output, [[[5.679, 5.679], [4.32, 4.32]]], atol=1e-3
      )
      self.assertAllClose(
          scores,
          [
              [
                  [[0.33, 0.67], [0.67, 0.33]],
                  [[0.33, 0.67], [0.67, 0.33]],
              ]
          ],
          atol=1e-3,
      )
  
file_path: keras/keras/layers/attention/grouped_query_attention_test.py
imports: import numpy
  import pytest
  from absl.testing import parameterized
  from keras import backend
  from keras import initializers
  from keras import layers
  from keras import testing
label_desc: Use image node_id 7 for calling the GroupedQueryAttentionTest obj's underlying member method code with example usage: obj.test_correctness() without return types