code: def validate_args(args):
      if not args.task.do_train and not args.task.do_eval:
          raise ValueError(
              "At least one of `do_train` or `do_eval` must be True."
          )
  
      if args.parameter.gradient_accumulation_steps < 1:
          raise ValueError(
              "Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
                  args.parameter.gradient_accumulation_steps
              )
          )
  
file_path: FARM/farm/experiment.py
imports: import logging
  from pathlib import Path
  from farm.data_handler.data_silo import DataSilo
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.language_model import LanguageModel
  from farm.modeling.optimization import initialize_optimizer
  from farm.modeling.prediction_head import PredictionHead
  from farm.modeling.tokenization import Tokenizer
  from farm.data_handler.processor import Processor
  from farm.train import Trainer, EarlyStopping
  from farm.utils import set_all_seeds, initialize_device_settings
  from farm.utils import MLFlowLogger
  from farm.file_utils import read_config, unnestConfig
label_desc: Use image node_id 4 for calling a global function with example usage: validate_args(args) without return types