code: def _set_up_model(self, config, pipeline, device):
      if config.get("use_identity") in ["True", True]:
          self._use_identity = True
          self._config = config
          self.config["batch_size"] = LemmaProcessor.DEFAULT_BATCH_SIZE
      else:
          # the lemmatizer only looks at one word when making
          # decisions, not the surrounding context
          # therefore, we can save some time by remembering what
          # we did the last time we saw any given word,pos
          # since a long running program will remember everything
          # (unless we go back and make it smarter)
          # we make this an option, not the default
          self.store_results = config.get("store_results", False)
          self._use_identity = False
          args = {
              "charlm_forward_file": config.get(
                  "forward_charlm_path", None
              ),
              "charlm_backward_file": config.get(
                  "backward_charlm_path", None
              ),
          }
          self._trainer = Trainer(
              args=args,
              model_file=config["model_path"],
              device=device,
              foundation_cache=pipeline.foundation_cache,
          )
  
file_path: stanza/stanza/pipeline/lemma_processor.py
imports: from itertools import compress
  import torch
  from stanza.models.common import doc
  from stanza.models.lemma.data import DataLoader
  from stanza.models.lemma.trainer import Trainer
  from stanza.pipeline._constants import *
  from stanza.pipeline.processor import UDProcessor, register_processor
label_desc: Use image node_id 3 for calling the LemmaProcessor obj's underlying member method code with example usage: obj._set_up_model(config, pipeline, device) without return types