code: def get_init_decode_state(self, x, given_length, attn_bias):
      x = self.norm(x)
  
      q, v, k, ff = self.input(x)
  
      full_length = x.shape[1]
      masked_tokens = full_length - given_length
  
      causal_mask = np.tril(np.ones((full_length, full_length)))
  
      bias = -1e10 * (1.0 - causal_mask)  # regular AR masking
      bias -= 1e10 * (
          jnp.arange(0, full_length) < masked_tokens
      )  # mask out zero tokens before context starts
      bias += attn_bias  # finally add attn bias for rpe
  
      attn_out = self.self_attn(q, v, k, bias)
      ff_out = self.glu(ff)
  
      return self.output(attn_out, ff_out), {
          "tokens_decoded": given_length.astype(jnp.uint32),
          "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 9 for calling the TransformerLayerShardV2 obj's underlying member method code with example usage: obj.get_init_decode_state(x, given_length, attn_bias) and returns: self,  dict