docstr: Test that the value and causal masks are taken into account.
docstr_tok: ["Test","that","the","value","and","causal","masks","are","taken","into","account","."]
code: def test_masking(self, use_causal_mask):
      """Test that the value and causal masks are taken into account."""
      layer = layers.GroupedQueryAttention(
          num_query_heads=2, num_key_value_heads=2, head_dim=2
      )
      query = np.array(
          [[1, 2, 3, 0, 0], [3, 3, 1, 1, 2], [1, 0, 0, 0, 0]]
      )
      masked_query = layers.Embedding(4, 8, mask_zero=True)(query)
      value = np.array([[5, 4, 0], [3, 0, 0], [2, 1, 1]])
      masked_value = layers.Embedding(6, 8, mask_zero=True)(value)
      output = layer(
          query=masked_query,
          value=masked_value,
          use_causal_mask=use_causal_mask,
      )
      mask = np.array(
          [[[1, 1, 0]] * 3 + [[0, 0, 0]] * 2]
          + [[[1, 0, 0]] * 5]
          + [[[1, 1, 1]] + [[0, 0, 0]] * 4]
      ).astype(bool)
      if use_causal_mask:
          mask = mask & np.array(
              [[[1, 0, 0], [1, 1, 0]] + [[1, 1, 1]] * 3]
          ).astype(bool)
      del masked_query._keras_mask
      del masked_value._keras_mask
      output_with_manual_mask = layer(
          query=masked_query, value=masked_value, attention_mask=mask
      )
      self.assertAllClose(output, output_with_manual_mask)
  
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 6 for calling the GroupedQueryAttentionTest obj's underlying member method code with example usage: obj.test_masking(use_causal_mask) without return types