code: def get_init_decode_state(self, x, given_length, attn_bias):
      x = f_psum(x)
      x = self.norm(x)
  
      q, v, k = self.qvk_proj(x)
  
      full_length = x.shape[0]
      masked_tokens = full_length - given_length
  
      seq_len = x.shape[0]
      causal_mask = np.tril(np.ones((seq_len, seq_len)))
  
      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)
      dense_out = self.ff(x)
  
      return g_psum(attn_out + dense_out), {
          "k": k,
          "v": v,
          "tokens_decoded": given_length.astype(jnp.uint32),
      }
  
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 7 for calling the TransformerLayerShard obj's underlying member method code with example usage: obj.get_init_decode_state(x, given_length, attn_bias) and returns: g_psum,  dict