class_docstr: TF version of ModelV2, which should contain a tf keras Model.
  
  Note that this class by itself is not a valid model unless you
  implement forward() in a subclass.
class_docstr_tok: ["TF","version","of","ModelV2",",","which","should","contain","a","tf","keras","Model",".","Note","that","this","class","by","itself","is","not","a","valid","model","unless","you","implement","forward","(",")","in","a","subclass","."]
docstr: Register the given list of variables with this model.
docstr_tok: ["Register","the","given","list","of","variables","with","this","model","."]
code: def register_variables(self, variables: List[TensorType]) -> None:
      """Register the given list of variables with this model."""
      if log_once("deprecated_tfmodelv2_register_variables"):
          deprecation_warning(
              old="TFModelV2.register_variables", error=False
          )
      self.var_list.extend(variables)
  
file_path: ray/rllib/models/tf/tf_modelv2.py
imports: import contextlib
  import gymnasium
  import re
  from typing import Dict, List, Union
  from ray.util import log_once
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.utils.annotations import override, PublicAPI
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.framework import try_import_tf
  from ray.rllib.utils.typing import ModelConfigDict, TensorType
label_desc: Use image node_id 4 for calling the TFModelV2 obj's underlying member method code with example usage: obj.register_variables(variables) without return types