code: def self_attn(self, q, v, k, attn_bias):
      k_rot = k[:, :, :, : self.d_rotary]
      k_pass = k[:, :, :, self.d_rotary :]
  
      q_rot = q[:, :, :, : self.d_rotary]
      q_pass = q[:, :, :, self.d_rotary :]
  
      sincos = fixed_pos_embedding(k_rot, seq_dim=1)
      q_rot = apply_rotary_pos_emb_v2(q_rot, sincos)
      k_rot = apply_rotary_pos_emb_v2(k_rot, sincos)
      q_rot = maybe_shard(q_rot, P("dp", None, "mp", None))
      k_rot = maybe_shard(k_rot, P("dp", None, "mp", None))
  
      k = jnp.concatenate([k_rot, k_pass], axis=-1)
      q = jnp.concatenate([q_rot, q_pass], axis=-1)
  
      k = maybe_shard(k, P("dp", None, "mp", None))
      q = maybe_shard(q, P("dp", None, "mp", None))
  
      attention_logits = jnp.einsum("bthd,bThd->bhtT", q, k)
  
      attention_logits = maybe_shard(
          attention_logits, P("dp", "mp", None, None)
      )
  
      sqrt_key_size = np.sqrt(self.d_head).astype(k.dtype)
      attention_logits = attention_logits / sqrt_key_size
  
      attention_logits += attn_bias
      attention_logits = maybe_shard(
          attention_logits, P("dp", "mp", None, None)
      )
  
      attention_weights = jax.nn.softmax(attention_logits)
      attention_weights = maybe_shard(
          attention_weights, P("dp", "mp", None, None)
      )
  
      attention_vec = jnp.einsum(
          "bhtT,bThd->bthd", attention_weights, v
      )
  
      attention_vec = maybe_shard(
          attention_vec, P("dp", None, "mp", None)
      )
      sharded_attn_vec = attention_vec.reshape(
          attention_vec.shape[:2]
          + (self.mp_num, self.n_head // self.mp_num, -1)
      )
      sharded_attn_vec = maybe_shard(
          sharded_attn_vec, P("dp", None, "mp", None, None)
      )
  
      attention_vec = attention_vec.reshape(
          sharded_attn_vec.shape[:2] + (self.mp_num, -1)
      )
      return maybe_shard(attention_vec, P("dp", None, "mp", None))
  
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 2 for calling the TransformerLayerShardV2 obj's underlying member method code with example usage: obj.self_attn(q, v, k, attn_bias) and returns: maybe_shard