code: def validate_config_for_pretrain(
      config: Config, msg: Printer
  ) -> None:
      if "tok2vec" not in config["nlp"]["pipeline"]:
          msg.warn(
              "No tok2vec component found in the pipeline. If your tok2vec "
              "component has a different name, you may need to adjust the "
              "tok2vec_model reference in the [pretraining] block. If you don't "
              "have a tok2vec component, make sure to add it to your [components] "
              "and the pipeline specified in the [nlp] block, so you can pretrain "
              "weights for it."
          )
  
imports: import re
  from enum import Enum
  from pathlib import Path
  from typing import List, Optional, Tuple
  import srsly
  from jinja2 import Template
  from thinc.api import Config
  from wasabi import Printer, diff_strings
  from ..None import util
  from ..language import DEFAULT_CONFIG_PRETRAIN_PATH
  from ..schemas import RecommendationSchema
  from ..util import SimpleFrozenList
  from ._util import COMMAND, Arg, Opt, import_code, init_cli, show_validation_error, string_to_list
label_desc: Use image node_id 7 for calling a global function with example usage: validate_config_for_pretrain(config, msg) without return types