code: def generate_once(self, new_tok, state):
      input_len = state[0]["v"].shape[0]
  
      if self.rpe is not None:
          attn_bias = self.rpe(
              input_len, input_len, self.heads_per_shard, 32
          )
          attn_bias = attn_bias[:, -1:, :]
      else:
          attn_bias = 0
  
      x = self.embed(new_tok)
  
      new_states = []
  
      for l, s in zip(self.transformer_layers, state):
          res, layer_state = l.decode_once(s, x, attn_bias)
          x = x + res
          new_states.append(layer_state)
  
      return self.proj(x), new_states
  
imports: import gc
  import random
  import time
  from functools import partial
  import haiku
  import jax
  import jax.numpy
  import numpy
  import optax
  from jax.experimental.maps import thread_resources
  from jax.experimental.pjit import pjit
  from mesh_transformer.checkpoint import read_ckpt, write_ckpt, write_ckpt_v2, load_ckpt_v2
  from mesh_transformer.layers import EmbeddingShard, TransformerLayerShard, RelativePositionEmbs, ProjectionShard, TransformerLayerShardV2, Projection, EmbeddingShardV2
  from mesh_transformer.util import to_f32, to_bf16, maybe_shard, head_print, global_norm
  from jax.experimental import PartitionSpec
label_desc: Use image node_id 5 for calling the CausalTransformerShard obj's underlying member method code with example usage: obj.generate_once(new_tok, state) and returns: self,  new_states