code: def __call__(
      self,
      input_ids,
      attention_mask=None,
      token_type_ids=None,
      position_ids=None,
      deterministic: bool = True,
  ):
      # Model
      encoder = FlaxPerformerModule(
          vocab_size=self.vocab_size,
          hidden_size=self.hidden_size,
          type_vocab_size=self.type_vocab_size,
          max_length=self.max_length,
          num_encoder_layers=self.num_encoder_layers,
          num_heads=self.num_heads,
          head_size=self.hidden_size,
          intermediate_size=self.intermediate_size,
          hidden_act=self.hidden_act,
          add_pooling_layer=False,
          name="bert",
      )(input_ids, attention_mask, token_type_ids, position_ids)
  
      # Compute the prediction scores
      encoder = nn.Dropout(rate=self.dropout_rate)(
          encoder, deterministic=deterministic
      )
      logits = FlaxBertOnlyMLMHead(
          vocab_size=self.vocab_size,
          hidden_act=self.hidden_act,
          name="cls",
          dtype=self.dtype,
      )(encoder)
  
      return (logits,)
  
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 FlaxPerformerForMaskedLMModule obj's underlying member method code with example usage: obj.__call__(input_ids, attention_mask, token_type_ids, position_ids, deterministic) and returns: logits