code: def masked_language_model_op(
      X,
      mlm_weights,
      mlm_ids,
      mlm_positions,
      params,
      featurizer_state,
      mode,
  ):
      return masked_language_model(
          X=X,
          mlm_weights=mlm_weights,
          mlm_ids=mlm_ids,
          mlm_positions=mlm_positions,
          config=params,
          embed_weights=featurizer_state["embed_weights"],
          hidden=featurizer_state["sequence_features"],
          train=(mode == tf.estimator.ModeKeys.TRAIN),
      )
  
file_path: finetune/finetune/model.py
imports: import logging
  import numpy
  import tensorflow
  from finetune.nn.target_blocks import language_model, masked_language_model
  from finetune.util.text_generation import sample_with_temperature
  from finetune.util.optimize_loss import optimize_loss
  from finetune.util.imbalance import class_weight_tensor
  from finetune.errors import FinetuneError
  from finetune.base_models import GPTModel, GPTModelSmall
label_desc: Use image node_id 2 for calling a global function with example usage: masked_language_model_op(X, mlm_weights, mlm_ids, mlm_positions, params, featurizer_state, mode) and returns: masked_language_model