code: def __post_init__(self):
      super().__post_init__()
      self._possible_values["metric"] = self.metric_class.names()
  
      self._possible_values[
          "metric_gpt_model"
      ] = possible_values.String(
          values=(
              "gpt-3.5-turbo-0301",
              "gpt-3.5-turbo-0613",
              "gpt-4-0314",
              "gpt-4-0613",
              "gpt-4-1106-preview",
          ),
          allow_custom=True,
      )
      self._possible_values[
          "metric_gpt_template"
      ] = possible_values.String(
          values=(f.split(".")[0] for f in os.listdir("prompts"))
      )
  
      self._possible_values["batch_size_inference"] = (0, 512, 1)
      self._possible_values["min_length_inference"] = (0, 1024, 1)
      self._possible_values["max_length_inference"] = (1, 4096, 1)
  
      self._possible_values["num_beams"] = (1, 4, 1)
      self._possible_values["temperature"] = (0, 10, 0.05)
      self._possible_values["repetition_penalty"] = (1, 10, 0.05)
      self._possible_values["top_k"] = (0, 100, 1)
      self._possible_values["top_p"] = (0.5, 1, 0.05)
      self._possible_values["num_history"] = (1, 50, 1)
  
      self._visibility["metric_class"] = -1
      # possible values for num_history are only used in chatbot tab
      self._visibility["num_history"] = -1
  
      self._nesting.add(
          ["metric_gpt_model", "metric_gpt_template"],
          [Dependency(key="metric", value="GPT", is_set=True)],
      )
  
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 ConfigNLPCausalLMPrediction obj's underlying member method code with example usage: obj.__post_init__() without return types