code: def __call__(self, intermediate_output, attention_output):
      hidden_state = nn.Dense(attention_output.shape[-1], name="dense")(
          intermediate_output
      )
      hidden_state = FlaxPerformerLayerNorm(name="layer_norm")(
          hidden_state + attention_output
      )
      return hidden_state
  
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 FlaxPerformerOutput obj's underlying member method code with example usage: obj.__call__(intermediate_output, attention_output) and returns: hidden_state