class_docstr: Construct the embeddings from word, position and token_type embeddings.
class_docstr_tok: ["Construct","the","embeddings","from","word",",","position","and","token_type","embeddings","."]
code: def __call__(
      self, input_ids, token_type_ids, position_ids, attention_mask
  ):
      # Embed
      w_emb = FlaxPerformerEmbedding(
          self.vocab_size, self.hidden_size, name="word_embeddings"
      )(jnp.atleast_2d(input_ids.astype("i4")))
      p_emb = FlaxPerformerEmbedding(
          self.max_length, self.hidden_size, name="position_embeddings"
      )(jnp.atleast_2d(position_ids.astype("i4")))
      t_emb = FlaxPerformerEmbedding(
          self.type_vocab_size,
          self.hidden_size,
          name="token_type_embeddings",
      )(jnp.atleast_2d(token_type_ids.astype("i4")))
  
      # Sum all embeddings
      summed_emb = w_emb + jnp.broadcast_to(p_emb, w_emb.shape) + t_emb
  
      # Layer Norm
      layer_norm = FlaxPerformerLayerNorm(name="layer_norm")(summed_emb)
  
      return layer_norm
  
imports: from typing import Callable, Dict, Tuple
  import numpy
  import flax.linen
  import jax
  import jax.numpy
  from jax.random import PRNGKey
  from modeling_flax_performer_utils import make_fast_softmax_attention
  from transformers.file_utils import add_start_docstrings
  from transformers.modeling_flax_utils import ACT2FN
  from transformers.models.bert.configuration_bert import BertConfig
  from transformers.models.bert.modeling_flax_bert import FlaxBertOnlyMLMHead, FlaxBertPreTrainedModel
  from transformers.utils import logging
label_desc: Use image node_id 1 for calling the FlaxPerformerEmbeddings obj's underlying member method code with example usage: obj.__call__(input_ids, token_type_ids, position_ids, attention_mask) and returns: layer_norm