code: def init_training_args(
      self,
      training_file: Optional[str] = None,
      validation_file: str = None,
      model: str = "curie",
      n_epochs: int = None,
      batch_size: int = None,
      learning_rate_multiplier: float = 0.1,
      prompt_loss_weight: float = 0.1,
      compute_classification_metrics: bool = False,
      classification_n_classes: int = None,
      classification_positive_class: str = None,
      classification_betas: list = None,
      suffix: str = None,
      hyperparameters: dict = None,
  ) -> None:
      self.trainer_kwargs["training_file"] = training_file
      self.trainer_kwargs["validation_file"] = validation_file
      self.trainer_kwargs["model"] = model
      self.trainer_kwargs["suffix"] = suffix
  
      if self.__legacy:
          if isinstance(self._record_class, TextClassificationRecord):
              self.trainer_kwargs["n_epochs"] = n_epochs or 4
          else:
              self.trainer_kwargs["n_epochs"] = n_epochs or 2
          self.trainer_kwargs["batch_size"] = batch_size
          self.trainer_kwargs[
              "learning_rate_multiplier"
          ] = learning_rate_multiplier
          self.trainer_kwargs["prompt_loss_weight"] = prompt_loss_weight
          self.trainer_kwargs[
              "compute_classification_metrics"
          ] = compute_classification_metrics
          self.trainer_kwargs[
              "classification_n_classes"
          ] = classification_n_classes
          self.trainer_kwargs[
              "classification_positive_class"
          ] = classification_positive_class
          self.trainer_kwargs[
              "classification_betas"
          ] = classification_betas
  
          if (
              isinstance(self._record_class, TextClassificationRecord)
              and self._eval_dataset
          ):
              label_schema = self._label_list
              if len(label_schema) == 2:
                  self.trainer_kwargs[
                      "classification_positive_class"
                  ] = label_schema[0]
                  self.trainer_kwargs[
                      "compute_classification_metrics"
                  ] = True
              else:
                  self.trainer_kwargs["classification_n_classes"] = len(
                      label_schema
                  )
                  self.trainer_kwargs[
                      "compute_classification_metrics"
                  ] = True
      else:
          if "hyperparameters":
              self.trainer_kwargs["hyperparameters"] = hyperparameters
          else:
              self.trainer_kwargs["hyperparameters"] = {}
              self.trainer_kwargs["hyperparameters"]["n_epochs"] = (
                  n_epochs or 1
              )
  
imports: import logging
  import os
  import time
  from typing import List, Optional, Union
  import numpy
  from argilla._constants import OPENAI_END_TOKEN, OPENAI_LEGACY_MODELS, OPENAI_SEPARATOR, OPENAI_WHITESPACE
  from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.datasets import TextClassificationSettings, TokenClassificationSettings
  from argilla.training.base import ArgillaTrainerSkeleton
  from argilla.training.utils import filter_allowed_args
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 3 for calling the ArgillaOpenAITrainer obj's underlying member method code with example usage: obj.init_training_args(training_file, validation_file, model, n_epochs, batch_size, learning_rate_multiplier, prompt_loss_weight, compute_classification_metrics, classification_n_classes, classification_positive_class, classification_betas, suffix, hyperparameters) without return types