code: def test_attention_alias_submodule(self):
      key1, key2 = random.split(random.key(0), 2)
      query = random.uniform(key1, (3, 5))
      key_value = random.uniform(key1, (9, 5))
      attention_kwargs = dict(
          num_heads=8,
          qkv_features=16,
          kernel_init=initializers.lecun_normal(),
          bias_init=initializers.uniform(),
          deterministic=False,
      )
  
      class Foo1(nn.Module):
          attention_kwargs: dict
  
          @nn.compact
          def __call__(self, query, key):
              return nn.MultiHeadDotProductAttention(
                  **self.attention_kwargs
              )(query, key)
  
      class Foo2(nn.Module):
          attention_kwargs: dict
  
          @nn.compact
          def __call__(self, query, key, value):
              return nn.MultiHeadAttention(**self.attention_kwargs)(
                  query, key, value
              )
  
      module1 = Foo1(attention_kwargs)
      module2 = Foo2(attention_kwargs)
      out1, v1 = module1.init_with_output(key2, query, key_value)
      out2, v2 = module2.init_with_output(
          key2, query, key_value, key_value
      )
  
      # test different output and variables if layer names are different
      self.assertTrue((out1 != out2).all())
      v2["params"]["MultiHeadDotProductAttention_0"] = v2["params"][
          "MultiHeadAttention_0"
      ]
      del v2["params"]["MultiHeadAttention_0"]
      self.assertTrue(
          jax.tree_util.tree_all(
              jax.tree_map(lambda x, y: (x != y).all(), v1, v2)
          )
      )
  
      # test same output if variables are the same
      v2 = jax.tree_map(lambda x: x, v1)
      v2["params"]["MultiHeadAttention_0"] = v2["params"][
          "MultiHeadDotProductAttention_0"
      ]
      del v2["params"]["MultiHeadDotProductAttention_0"]
      out2 = module2.apply(v2, query, key_value, key_value)
      self.assertTrue((out1 == out2).all())
  
      # test same output and variables if names are the same
      class Foo2(nn.Module):
          attention_kwargs: dict
  
          @nn.compact
          def __call__(self, query, key, value):
              return nn.MultiHeadAttention(
                  **self.attention_kwargs,
                  name="MultiHeadDotProductAttention_0",
              )(query, key, value)
  
      module2 = Foo2(attention_kwargs)
      out2, v2 = module2.init_with_output(
          key2, query, key_value, key_value
      )
      self.assertTrue((out1 == out2).all())
      self.assertTrue(
          jax.tree_util.tree_all(
              jax.tree_map(lambda x, y: (x == y).all(), v1, v2)
          )
      )
  
imports: import jax
  import jax.numpy
  import numpy
  from absl.testing import absltest, parameterized
  from jax import lax, random
  from jax.nn import initializers
  from flax import errors, jax_utils
  from flax import linen
  from flax.core import pop
label_desc: Use image node_id 15 for calling the AttentionTest obj's underlying member method code with example usage: obj.test_attention_alias_submodule() and returns: nn, nn, nn