class_docstr: Resolver for HuggingFace's pipeline request and responses, providing necessary data transformations and formatting.
  
  This is based off (from wandb.sdk.integration_utils.auto_logging import RequestResponseResolver)
class_docstr_tok: ["Resolver","for","HuggingFace","'s","pipeline","request","and","responses",",","providing","necessary","data","transformations","and","formatting",".","This","is","based","off","(","from","wandb.sdk.integration_utils.auto_logging","import","RequestResponseResolver",")"]
docstr: Transform input and response data based on specific tasks.
  
  :param task: the task name
  :param input_data: the input data
  :param response: the response data
  :returns: tuple of transformed input_data and response
docstr_tok: ["Transform","input","and","response","data","based","on","specific","tasks",".",":","param","task",":","the","task","name",":","param","input_data",":","the","input","data",":","param","response",":","the","response","data",":","returns",":","tuple","of","transformed","input_data","and","response"]
code: def _transform_task_specific_data(
      task: str,
      input_data: Union[List[Any], Any],
      response: Union[List[Any], Any],
  ) -> Tuple[Union[List[Any], Any], Union[List[Any], Any]]:
      """Transform input and response data based on specific tasks.
  
      :param task: the task name
      :param input_data: the input data
      :param response: the response data
      :returns: tuple of transformed input_data and response
      """
      if task == "question-answering":
          input_data = (
              input_data
              if isinstance(input_data, list)
              else [input_data]
          )
          input_data = [data.__dict__ for data in input_data]
      elif task == "conversational":
          # We only grab the latest input/output pair from the conversation
          # Logging the whole conversation renders strangely.
          input_data = (
              input_data
              if isinstance(input_data, list)
              else [input_data]
          )
          input_data = [
              data.__dict__["past_user_inputs"][-1]
              for data in input_data
          ]
  
          response = (
              response if isinstance(response, list) else [response]
          )
          response = [
              data.__dict__["generated_responses"][-1]
              for data in response
          ]
      return input_data, response
  
file_path: wandb/wandb/integration/huggingface/resolver.py
imports: import logging
  import os
  from datetime import datetime
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import pytz
  import wandb
  from wandb.sdk.integration_utils.auto_logging import Response
  from wandb.sdk.lib.runid import generate_id
label_desc: Use image node_id 3 for calling the HuggingFacePipelineRequestResponseResolver obj's underlying member method code with example usage: obj._transform_task_specific_data(task, input_data, response) and returns: input_data,  response