class_docstr: Mixin to add task template functionality to a `FeedbackDataset`.
  The NLP tasks covered are:
      "text_classification"
      "extractive_question_answering"
      "summarization"
      "translation"
      "sentence_similarity"
      "natural_language_inference"
      "supervised_fine_tuning"
      "preference_modeling/reward_modeling"
      "proximal_policy_optimization"
      "direct_preference_optimization"
      "retrieval_augmented_generation"
      "multi_modal_classification"
      "multi_modal_transcription"
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docstr: You can use this method to create a basic dataset for proximal policy optimization tasks.
  
  Args:
      rating_scale: Set this parameter to the number of relevancy scale you want to add to your dataset
      context: Set this parameter to True if you want to add context to your dataset
      use_markdown: Set this parameter to True if you want to use markdown in your dataset
      guidelines: Contains the guidelines for the dataset
      metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
      vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.
  
  Returns:
      A `FeedbackDataset` object for proximal policy optimization containing "context" and "action" fields and a LabelQuestion named "label"
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code: def for_proximal_policy_optimization(
      cls: Type["FeedbackDataset"],
      rating_scale: int = 7,
      context: bool = False,
      use_markdown: bool = False,
      guidelines: Optional[str] = None,
      metadata_properties: List[AllowedMetadataPropertyTypes] = None,
      vectors_settings: List[VectorSettings] = None,
  ) -> "FeedbackDataset":
      """
      You can use this method to create a basic dataset for proximal policy optimization tasks.
  
      Args:
          rating_scale: Set this parameter to the number of relevancy scale you want to add to your dataset
          context: Set this parameter to True if you want to add context to your dataset
          use_markdown: Set this parameter to True if you want to use markdown in your dataset
          guidelines: Contains the guidelines for the dataset
          metadata_properties: Contains the metadata properties that will be indexed and could be used to filter the dataset. Defaults to `None`.
          vectors_settings: Define the configuration of the vectors associated to the records that will be used to perform the vector search. Defaults to `None`.
  
      Returns:
          A `FeedbackDataset` object for proximal policy optimization containing "context" and "action" fields and a LabelQuestion named "label"
      """
      default_guidelines = "This is a proximal policy optimization dataset that contains contexts and prompts. Please choose the label that best describes prompt."
      fields = [TextField(name="prompt", use_markdown=use_markdown)]
      if context:
          fields.append(
              TextField(
                  name="context",
                  use_markdown=use_markdown,
                  required=False,
              )
          )
  
      return cls(
          fields=fields,
          questions=[
              RatingQuestion(
                  name="prompt",
                  values=list(range(1, rating_scale + 1)),
                  description="Choose one of the labels that best describes the prompt.",
              )
          ],
          guidelines=default_guidelines
          if guidelines is None
          else guidelines,
          metadata_properties=metadata_properties,
          vectors_settings=vectors_settings,
      )
  
imports: import logging
  from typing import TYPE_CHECKING, List, Literal, Optional, Type, Union
  from uuid import UUID
  from argilla.client.feedback.dataset.helpers import get_dataset_by_name_and_workspace
  from argilla.client.feedback.dataset.remote.dataset import INCLUDE_ALL_VECTORS_PARAM, RemoteFeedbackDataset
  from argilla.client.feedback.schemas.enums import FieldTypes, MetadataPropertyTypes, QuestionTypes
  from argilla.client.feedback.schemas.fields import TextField
  from argilla.client.feedback.schemas.questions import LabelQuestion, MultiLabelQuestion, RankingQuestion, RatingQuestion, TextQuestion
  from argilla.client.feedback.schemas.remote.fields import RemoteTextField
  from argilla.client.feedback.schemas.remote.metadata import RemoteFloatMetadataProperty, RemoteIntegerMetadataProperty, RemoteTermsMetadataProperty
  from argilla.client.feedback.schemas.remote.questions import RemoteLabelQuestion, RemoteMultiLabelQuestion, RemoteRankingQuestion, RemoteRatingQuestion, RemoteTextQuestion
  from argilla.client.feedback.schemas.types import AllowedMetadataPropertyTypes
  from argilla.client.feedback.schemas.vector_settings import VectorSettings
  from argilla.client.sdk.commons.errors import AlreadyExistsApiError
  from argilla.client.sdk.v1.datasets import api
  from argilla.client.singleton import ArgillaSingleton
  from argilla.client.workspaces import Workspace
label_desc: Use image node_id 9 for calling the TaskTemplateMixin obj's underlying member method code with example usage: obj.for_proximal_policy_optimization(cls, rating_scale, context, use_markdown, guidelines, metadata_properties, vectors_settings) and returns: cls