code: def __call__(self, x, attn_bias):
      x = self.norm(x)
  
      q, v, k, ff = self.input(x)
  
      # head_print("x.shape", x.shape)
      # head_print("attn_bias.shape", attn_bias.shape)
  
      seq_len = x.shape[1]
      causal_mask = np.tril(np.ones((seq_len, seq_len)))[None, :, :]
      bias = -1e10 * (1.0 - causal_mask)
  
      # head_print("bias.shape", bias.shape)
  
      bias += attn_bias
  
      attn_out = self.self_attn(q, v, k, bias)
      ff_out = self.glu(ff)
  
      return self.output(attn_out, ff_out)
  
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 6 for calling the TransformerLayerShardV2 obj's underlying member method code with example usage: obj.__call__(x, attn_bias) and returns: self