code: def __call__(
      self, input_ids, token_type_ids, position_ids, attention_mask
  ):
      # Embedding
      embeddings = FlaxPerformerEmbeddings(
          self.vocab_size,
          self.hidden_size,
          self.type_vocab_size,
          self.max_length,
          name="embeddings",
      )(input_ids, token_type_ids, position_ids, attention_mask)
  
      # N stacked encoding layers
      encoder = FlaxPerformerEncoder(
          self.num_encoder_layers,
          self.num_heads,
          self.head_size,
          self.intermediate_size,
          hidden_act=self.hidden_act,
          name="encoder",
      )(embeddings, attention_mask)
  
      if not self.add_pooling_layer:
          return encoder
  
      pooled = FlaxPerformerPooler(name="pooler")(encoder)
      return encoder, pooled
  
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 FlaxPerformerModule obj's underlying member method code with example usage: obj.__call__(input_ids, token_type_ids, position_ids, attention_mask) and returns: encoder,  pooled, encoder