code: def __call__(self, hidden_state, attention_mask):
      single_head_dim = self.head_size // self.num_heads
      fast_softmax_attention = make_fast_softmax_attention(
          qkv_dim=single_head_dim
      )
      self_att = nn.attention.SelfAttention(
          num_heads=self.num_heads,
          qkv_features=self.head_size,
          name="self",
          attention_fn=fast_softmax_attention,
      )(hidden_state, attention_mask)
  
      layer_norm = FlaxPerformerLayerNorm(name="layer_norm")(
          self_att + hidden_state
      )
      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 FlaxPerformerAttention obj's underlying member method code with example usage: obj.__call__(hidden_state, attention_mask) and returns: layer_norm