code: def __post_init__(self):
      self.prompt_column = (
          tuple(
              self.prompt_column,
          )
          if isinstance(self.prompt_column, str)
          else tuple(self.prompt_column)
      )
      super().__post_init__()
  
      self._possible_values["train_dataframe"] = possible_values.Files(
          prefer_with=lambda path: "train" in path
      )
      self._possible_values[
          "validation_strategy"
      ] = possible_values.String(
          values=(
              ("custom", "Custom holdout validation"),
              ("automatic", "Automatic holdout validation"),
          ),
          allow_custom=False,
      )
      self._possible_values[
          "validation_dataframe"
      ] = possible_values.Files(
          add_none=True, prefer_with=lambda path: "val" in path
      )
      self._possible_values["validation_size"] = (0.01, 0.95, 0.01)
      self._possible_values["data_sample"] = (0.01, 1, 0.01)
      self._possible_values["data_sample_choice"] = [
          "Train",
          "Validation",
      ]
      self._possible_values["system_column"] = possible_values.Columns(
          prefer_with=lambda column: column in ("system",),
          add_none=True,
      )
      self._possible_values["prompt_column"] = possible_values.Columns(
          prefer_with=lambda column: column in ("instruction", "prompt")
      )
      self._possible_values["answer_column"] = possible_values.Columns(
          prefer_with=lambda column: column in ("answer", "output")
      )
      self._possible_values[
          "parent_id_column"
      ] = possible_values.Columns(
          prefer_with=lambda column: column in ("parent",),
          add_none=True,
      )
  
      self._nesting.add(
          ["chatbot_name", "chatbot_author"],
          [Dependency(key="personalize", value=True, is_set=True)],
      )
  
      self._nesting.add(
          ["validation_dataframe"],
          [
              Dependency(
                  key="validation_strategy", value="custom", is_set=True
              )
          ],
      )
  
      self._nesting.add(
          ["validation_size"],
          [
              Dependency(
                  key="validation_strategy",
                  value="automatic",
                  is_set=True,
              )
          ],
      )
  
      self._nesting.add(
          ["data_sample_choice"],
          [Dependency(key="data_sample", value=1, is_set=False)],
      )
  
      self._nesting.add(
          ["limit_chained_samples"],
          [
              Dependency(
                  key="parent_id_column", value="None", is_set=False
              )
          ],
      )
  
      self._nesting.add(
          ["text_system_start", "add_eos_token_to_system"],
          [Dependency(key="system_column", value="None", is_set=False)],
      )
  
      self._visibility["dataset_class"] = -1
  
imports: import multiprocessing
  import os
  from dataclasses import dataclass, field
  from typing import Any, Dict, List, Tuple
  import torch
  import llm_studio.src.datasets.text_causal_language_modeling_ds
  from llm_studio.python_configs.base import DefaultConfig, DefaultConfigProblemBase
  from llm_studio.src import possible_values
  from llm_studio.src.augmentations.nlp_aug import BaseNLPAug
  from llm_studio.src.loggers import Loggers
  from llm_studio.src.losses import text_causal_language_modeling_losses
  from llm_studio.src.metrics import text_causal_language_modeling_metrics
  from llm_studio.src.models import text_causal_language_modeling_model
  from llm_studio.src.nesting import Dependency
  from llm_studio.src.optimizers import Optimizers
  from llm_studio.src.plots import text_causal_language_modeling_plots
  from llm_studio.src.schedulers import Schedulers
  from llm_studio.src.utils.modeling_utils import generate_experiment_name
label_desc: Use image node_id 1 for calling the ConfigNLPCausalLMDataset obj's underlying member method code with example usage: obj.__post_init__() without return types