code: def generate_initial(self, context, length):
      # slice last token off the context (we use that in generate_once to generate the first new token)
      last = context[-1:]
      context = context[:-1]
  
      input_len = context.shape[0]
  
      if self.rpe is not None:
          attn_bias = self.rpe(
              input_len, input_len, self.heads_per_shard, 32
          )
      else:
          attn_bias = 0
  
      x = self.embed(context)
  
      states = []
  
      for l in self.transformer_layers:
          res, layer_state = l.get_init_decode_state(
              x, length - 1, attn_bias
          )
          x = x + res
          states.append(layer_state)
  
      return self.proj(x), (
          last.astype(jnp.uint32),
          states,
          hk.next_rng_key(),
      )
  
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 4 for calling the CausalTransformerShard obj's underlying member method code with example usage: obj.generate_initial(context, length) and returns: self,  last,  states,  hk