code: def __call__(
      self,
      input_ids,
      attention_mask=None,
      token_type_ids=None,
      position_ids=None,
      params: dict = None,
      train: bool = False,
      dropout_rng: PRNGKey = None,
  ):
      (
          input_ids,
          attention_mask,
          token_type_ids,
          position_ids,
      ) = self._check_inputs(
          input_ids, attention_mask, token_type_ids, position_ids
      )
  
      # Handle any PRNG if needed
      rngs = {}
      if dropout_rng is not None:
          rngs["dropout"] = dropout_rng
  
      return self.module.apply(
          {"params": params or self.params},
          jnp.array(input_ids, dtype="i4"),
          jnp.array(attention_mask, dtype="i4"),
          jnp.array(token_type_ids, dtype="i4"),
          jnp.array(position_ids, dtype="i4"),
          not train,
          rngs=rngs,
      )
  
imports: from typing import Callable, Dict, Tuple
  import flax.linen
  import jax
  import jax.numpy
  import 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 2 for calling the FlaxPerformerForMaskedLM obj's underlying member method code with example usage: obj.__call__(input_ids, attention_mask, token_type_ids, position_ids, params, train, dropout_rng) and returns: self