docstr: Tests autoregresive masking for 1d attention.
docstr_tok: ["Tests","autoregresive","masking","for","1d","attention","."]
code: def test_causal_mask_1d(self):
      """Tests autoregresive masking for 1d attention."""
      x = jnp.ones((3, 16))  # (bs1, length)
      mask_1d = nn.attention.make_causal_mask(x)
      ts = np.arange(16)
      mask_1d_simple = (ts[:, None] >= ts[None, :])[None, None, :, :]
      mask_1d_simple = jnp.broadcast_to(mask_1d_simple, (3, 1, 16, 16))
      np.testing.assert_allclose(
          mask_1d,
          mask_1d_simple,
      )
  
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 7 for calling the AttentionTest obj's underlying member method code with example usage: obj.test_causal_mask_1d() without return types