code: def init_training_args(self):
      import torch
      from transformers import TrainingArguments
  
      def convert_to_floats(dataset):
          dataset.set_format("torch")
          return dataset.map(
              lambda x: {"float_label": x["label"].to(torch.float)},
              remove_columns=["label"],
          ).rename_column("float_label", "label")
  
      self.model_kwargs = {}
      self.model_kwargs["pretrained_model_name_or_path"] = (
          self._model
          if isinstance(self._model, str)
          else self._model.config._name_or_path
      )
  
      if self._record_class in [
          TextClassificationRecord,
          TokenClassificationRecord,
      ]:
          self.model_kwargs["num_labels"] = len(self._label_list)
          self.model_kwargs["id2label"] = self._id2label
          self.model_kwargs["label2id"] = self._label2id
          if self._multi_label:
              self.model_kwargs[
                  "problem_type"
              ] = "multi_label_classification"
  
          if self._record_class == TextClassificationRecord:
              columns_mapping = {
                  "text": "text",
                  "label": "binarized_label",
              }
              if self._multi_label:
                  self._train_dataset = _apply_column_mapping(
                      self._train_dataset, columns_mapping
                  )
                  self._train_dataset = convert_to_floats(
                      self._train_dataset
                  )
  
                  if self._eval_dataset is not None:
                      self._eval_dataset = _apply_column_mapping(
                          self._eval_dataset, columns_mapping
                      )
                      self._eval_dataset = convert_to_floats(
                          self._eval_dataset
                      )
  
      self.trainer_kwargs = get_default_args(TrainingArguments.__init__)
      self.trainer_kwargs["evaluation_strategy"] = (
          "no" if self._eval_dataset is None else "epoch"
      )
      self.trainer_kwargs["logging_steps"] = 30
      self.trainer_kwargs["logging_steps"] = 1
      self.trainer_kwargs["num_train_epochs"] = 1
  
file_path: argilla/src/argilla/training/transformers.py
imports: import logging
  from typing import List, Union
  from datasets import DatasetDict
  from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.training.base import ArgillaTrainerSkeleton
  from argilla.training.utils import _apply_column_mapping, filter_allowed_args, get_default_args
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 2 for calling the ArgillaTransformersTrainer obj's underlying member method code with example usage: obj.init_training_args() and returns: dataset