class_docstr: Schema for the `FeedbackDataset` text questions, which are the ones that will
  require a text response from the user.
  
  Args:
      type: The type of the question. Defaults to 'text' and cannot/shouldn't be
          modified.
      use_markdown: Whether the question should be rendered using markdown or not.
          Defaults to False.
  
  Examples:
      >>> from argilla.client.feedback.schemas.questions import TextQuestion
      >>> TextQuestion(name="text_question", title="Text Question")
class_docstr_tok: ["Schema","for","the","`","FeedbackDataset","`","text","questions",",","which","are","the","ones","that","will","require","a","text","response","from","the","user",".","Args",":","type",":","The","type","of","the","question",".","Defaults","to","'text","'","and","can","not","\/should","n't","be","modified",".","use_markdown",":","Whether","the","question","should","be","rendered","using","markdown","or","not",".","Defaults","to","False",".","Examples",":",">",">",">","from","argilla.client.feedback.schemas.questions","import","TextQuestion",">",">",">","TextQuestion","(","name=","''","text_question","''",",","title=","''","Text","Question","''",")"]
code: def server_settings(self) -> Dict[str, Any]:
      return {
          "type": self.type,
          "use_markdown": self.use_markdown,
      }
  
imports: import warnings
  from abc import ABC, abstractmethod
  from typing import Any, Dict, List, Literal, Optional, Union
  from argilla.client.feedback.schemas.enums import QuestionTypes
  from argilla.client.feedback.schemas.utils import LabelMappingMixin
  from argilla.client.feedback.schemas.validators import title_must_have_value
  from argilla.pydantic_v1 import BaseModel, Extra, Field, conint, conlist, root_validator, validator
label_desc: Use image node_id 1 for calling the TextQuestion obj's underlying member method code with example usage: obj.server_settings() and returns: dict