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 Any, Dict, Optional, Tuple
  import datasets
  import evaluate
  import torch
  import transformers
  from datasets import load_dataset
  from torch.utils.tensorboard import SummaryWriter
  from transformers import CONFIG_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, Trainer, TrainingArguments, default_data_collator, is_torch_tpu_available, set_seed
  from transformers.integrations import TensorBoardCallback
  from transformers.testing_utils import CaptureLogger
  from transformers.trainer_utils import get_last_checkpoint
  from transformers.utils import check_min_version, send_example_telemetry
  from transformers.utils.versions import require_version
  import determined
  from determined.pytorch import dsat
  from determined.transformers import DetCallback
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