code: def __call__(
      self, cfg: Config, x_q, x_kv, mask=None, *, rngs: nnx.Rngs
  ):
      q = jnp.einsum(
          "bse,enh->bsnh", x_q, self.WQ.astype(cfg.dtype)
      ).astype(jnp.float32)
      k = jnp.einsum(
          "bte,enh->btnh", x_kv, self.WK.astype(cfg.dtype)
      ).astype(jnp.float32)
      v = jnp.einsum("bte,enh->btnh", x_kv, self.WV.astype(cfg.dtype))
  
      index = None
      if cfg.decode:
          index = self.index
          one_hot_indices = jax.nn.one_hot(
              self.index, cfg.max_length, dtype=cfg.dtype
          )
          self.key = (
              self.key + jnp.moveaxis(k, -3, -1) * one_hot_indices
          )
          self = self + jnp.moveaxis(v, -3, -1) * one_hot_indices
          k = jnp.moveaxis(self.key, -1, -3)
          v = jnp.moveaxis(self, -1, -3)
          cache_mask = jnp.broadcast_to(
              jnp.arange(cfg.max_length) <= self.index,
              (cfg.batch, 1, 1, cfg.max_length),
          )
          mask = jnp.logical_and(
              cache_mask if mask is None else mask, cache_mask
          ).astype(cfg.dtype)
          self.index = self.index + 1
  
      attention_bias = 0.0
      if mask is None:  # Hack in lieu of general mask routing.
          mask = make_causal_mask(x, jnp.float32)
      if mask is not None:
          attention_bias = jax.lax.select(
              mask > 0,
              jnp.full(mask.shape, 0.0, cfg.dtype),
              jnp.full(mask.shape, -1e10, cfg.dtype),
          )
  
      sin, cos = sine_table(q.shape[-1], max(q.shape[1], k.shape[1]))
      q, k = apply_rotary_embedding(q, k, cos, sin, index=index)
  
      l = (
          jnp.einsum("bsnh,btnh->bnst", q, k) / np.sqrt(cfg.depth)
          + attention_bias
      )
      s = jax.nn.softmax(l).astype(cfg.dtype)
      s = dropout(cfg, s, rngs=rngs)
      a = jnp.einsum("bnst,btnh->bsnh", s, v)
      o = jnp.einsum("bsnh,nhe->bse", a, self.WO.astype(cfg.dtype))
  
      return o
  
imports: import dataclasses
  import typing
  import jax
  import jax.numpy
  import numpy
  from jax.sharding import PartitionSpec
  from flax.experimental import nnx
label_desc: Use image node_id 2 for calling the Attention obj's underlying member method code with example usage: obj.__call__(cfg, x_q, x_kv, mask) and returns: o