code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      self.project_name = (
          f"{self._workspace}_{self._name}_{str(uuid4())[:8]}"
      )
  
      if self._seed:
          self._logger.warning(
              "Setting a seed is not supported by `autotrain-advanced`."
          )
          self._seed = 42
  
      if self._model is None:
          self._model = "bert-base-uncased"
          self._logger.warning(
              f"No model defined. Using the default model {self._model}."
          )
  
      data_dict = {}
      self._num_samples = 0
      if isinstance(self._dataset, DatasetDict):
          self._train_dataset = self._dataset["train"]
          self._eval_dataset = self._dataset["test"]
          data_dict["valid_data"] = [self._eval_dataset.to_pandas()]
          self._num_samples += len(self._eval_dataset)
      else:
          self._train_dataset = self._dataset
          self._eval_dataset = None
          data_dict["valid_data"] = []
      data_dict["train_data"] = [self._train_dataset.to_pandas()]
      self._num_samples += len(self._train_dataset)
  
      if self._record_class == TextClassificationRecord:
          if self._multi_label:
              raise NotImplementedError(
                  "TextClassificaiton `multi_label=True` is not supported by `autotrain-advanced`."
              )
          elif self._multi_label is False:
              n_classes = len(
                  self._train_dataset.features["label"].names
              )
              self.task = (
                  "text_multi_class_classification"
                  if n_classes > 2
                  else "text_binary_classification"
              )
              self._id2label = dict(
                  enumerate(self._train_dataset.features["label"].names)
              )
              self._label_list = self._train_dataset.features[
                  "label"
              ].names
          self._label2id = {v: k for k, v in self._id2label.items()}
      elif self._record_class == TokenClassificationRecord:
          raise NotImplementedError(
              "`Text2Text` and `TokenClassification` tasks are not supported by `autotrain-advanced`."
          )
  
      self.init_training_args()
  
      self.prepare_dataset(data_dict=data_dict)
      self.initialize_project()
  
imports: import logging
  import os
  from typing import List, Optional, Union
  from uuid import uuid4
  from datasets import DatasetDict
  from argilla.client.models import TextClassificationRecord, TokenClassificationRecord
  from argilla.training.base import ArgillaTrainerSkeleton
  from argilla.utils.dependency import require_dependencies
label_desc: Use image node_id 1 to create a new ArgillaAutoTrainTrainer object from inherited base classes: ArgillaTrainerSkeleton, AutoTrainMixin with example: obj = ArgillaAutoTrainTrainer()