class_docstr: Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
class_docstr_tok: ["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 torch
  import transformers
  from transformers import CONFIG_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, TrainingArguments, default_data_collator, set_seed
  from transformers.testing_utils import CaptureLogger
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version
  from transformers.utils.versions import require_version
  from ls_hf_gpt_layer import inject_ls_layer
  from examples.training.huggingface.gcq import LSTrainer, GCQArguments
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