class_docstr: Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
code: def __post_init__(self):
      if self.config_overrides is not None and (
          self.config_name is not None
          or self.model_name_or_path is not None
      ):
          raise ValueError(
              "--config_overrides can't be used in combination with --config_name or --model_name_or_path"
          )
  
imports: import logging
  import math
  import os
  import sys
  from dataclasses import dataclass, field
  from itertools import chain
  from typing import Optional
  import datasets
  from datasets import load_dataset
  import adapters
  import evaluate
  import transformers
  from adapters import AdapterArguments, AdapterTrainer, setup_adapter_training
  from transformers import CONFIG_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, AutoConfig, AutoModelForMaskedLM, AutoTokenizer, DataCollatorForLanguageModeling, HfArgumentParser, Trainer, TrainingArguments, is_torch_tpu_available, set_seed
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
label_desc: Use image node_id 1 for calling the ModelArguments obj's underlying member method code with example usage: obj.__post_init__() without return types