code: def input(self, x):
      # [batch, seq, dim]
      projected = self.input_proj(x)
  
      # [batch, seq, mp, dim//mp]
      projected = maybe_shard(projected, P("dp", None, "mp"))
      mp_split = jnp.reshape(
          projected, projected.shape[:-1] + (self.mp_num, -1)
      )
      mp_split = maybe_shard(mp_split, P("dp", None, "mp", None))
  
      local_dim = self.d_head * self.n_head // self.mp_num
  
      q, v, k, ff = jnp.split(
          mp_split, [local_dim, local_dim * 2, local_dim * 3], axis=-1
      )
  
      q = self.head_split(q)
      v = self.head_split(v)
      k = self.head_split(k)
  
      return q, v, k, ff
  
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 4 for calling the TransformerLayerShardV2 obj's underlying member method code with example usage: obj.input(x) and returns: q,  v,  k,  ff