code: def language_model_op(X, params, featurizer_state, mode, encoder):
      language_model_state = language_model(
          X=X,
          sequence_lengths=featurizer_state["lengths"],
          config=params,
          embed_weights=featurizer_state["embed_weights"],
          hidden=featurizer_state["sequence_features"],
          train=mode == tf.estimator.ModeKeys.TRAIN,
      )
      lm_logits = language_model_state["logits"]
  
      if lm_logits is not None:
          lm_logit_mask = np.zeros(
              [1, lm_logits.get_shape().as_list()[-1]], dtype=np.float32
          )
          lm_logit_mask[:, encoder.vocab_size :] = -np.inf
  
          if "use_extra_toks" in params and not params.use_extra_toks:
              lm_logit_mask[:, encoder.start_token] = -np.inf
              lm_logit_mask[:, encoder.delimiter_token] = -np.inf
              lm_logit_mask[:, encoder.end_token] = -np.inf
  
              lm_logits += lm_logit_mask
          lm_predict_op = sample_with_temperature(
              lm_logits, params.lm_temp
          )
      else:
          lm_predict_op = tf.no_op()
      return lm_predict_op, language_model_state
  
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 1 for calling a global function with example usage: language_model_op(X, params, featurizer_state, mode, encoder) and returns: lm_predict_op,  language_model_state