class_docstr: A stack of encoder layers.
docstr: transform the encoded representation.
code: def __call__(self, encoded_emb, encoder_mask=None, use_dropout=True):
      """transform the encoded representation."""
      cfg = self.t5_config
      assert encoded_emb.ndim == 3  # [batch, length, emb_dim]
      x = encoded_emb
      if encoder_mask is not None:
          encoder_mask = t5_layers.make_attention_mask(
              encoder_mask, encoder_mask, dtype=cfg.dtype
          )
  
      rel_emb = t5_layers.RelativePositionBiases(
          num_buckets=32,
          max_distance=128,
          num_heads=self.num_heads,
          dtype=self.dtype,
          embedding_init=nn.initializers.variance_scaling(
              1.0, "fan_avg", "uniform"
          ),
      )
      for _ in range(
          cfg.num_encoder_layers - self.num_head_layers,
          cfg.num_encoder_layers,
      ):
          # [batch, length, emb_dim] -> [batch, length, emb_dim]
          x = t5_network.EncoderLayer(
              config=cfg, relative_embedding=rel_emb
          )(x, encoder_mask, deterministic=not use_dropout)
      x = t5_layers.LayerNorm(dtype=cfg.dtype)(x[:, 0, :])
      return l2_normalize(self.out_head(x), axis=-1)
  
imports: import functools
  from typing import Optional, Sequence
  from flax import linen
  import jax
  import jax.numpy
  from scenic.projects.knowledge_visual_language.models import constants
  from t5x.examples.t5 import layers
  from t5x.examples.t5 import network
label_desc: Use image node_id 2 for calling the TransformerHead obj's underlying member method code with example usage: obj.__call__(encoded_emb, encoder_mask, use_dropout) and returns: l2_normalize