code: def decode_once(self, decode_state, x, attn_bias):
      x = self.norm(x)
  
      assert x.shape[0] == 1
  
      q, v, k, ff = self.input(x)
  
      # add new kv to end
      v = jnp.concatenate((decode_state["v"], v), axis=1)[1:]
      k = jnp.concatenate((decode_state["k"], k), axis=1)[1:]
  
      tokens_decoded = decode_state["tokens_decoded"] + 1
      length = v.shape[1]
  
      masked_tokens = length - tokens_decoded
  
      attention_mask = jnp.arange(0, length) < masked_tokens
      bias = -1e10 * attention_mask
      bias += attn_bias
  
      attn_out = self.self_attn(q, v, k, bias)
      ff_out = self.glu(ff)
  
      return self.output(attn_out, ff_out), {
          "tokens_decoded": tokens_decoded,
          "k": k,
          "v": v,
      }
  
imports: import haiku
  import jax
  import jax.numpy
  import numpy
  from einops import rearrange, repeat
  from mesh_transformer.util import f_psum, g_psum, maybe_shard, head_print
  from jax.experimental import PartitionSpec
  from jax.experimental.maps import thread_resources
label_desc: Use image node_id 8 for calling the TransformerLayerShardV2 obj's underlying member method code with example usage: obj.decode_once(decode_state, x, attn_bias) and returns: self,  dict