class_docstr: Each answer choice sentence is scored independently, where the score is the sentence probability
  normalized by the unconditional sentence probability.
  For more details, refer to Section 2.4 of the GPT-3 paper (https://arxiv.org/pdf/2005.14165.pdf).
code: def generate_requests(
      self, eval_instance: Instance
  ) -> List[RequestState]:
      request_states: List[RequestState] = []
  
      for reference_index, reference in enumerate(
          eval_instance.references
      ):
          # original
          prompt = self.construct_prompt(
              self.train_instances,
              eval_instance,
              include_output=True,
              reference_index=reference_index,
          )
          request_states.append(
              self.construct_request_state(
                  prompt, reference_index, eval_instance
              )
          )
  
          # calibration
          # Compute the logprobs of the reference without train instances and the input question.
          prompt = self.construct_prompt(
              [],
              replace(eval_instance, input=Input(text="Answer:")),
              include_output=True,
              reference_index=reference_index,
          )
          request_states.append(
              self.construct_request_state(
                  prompt,
                  reference_index,
                  eval_instance,
                  request_mode="calibration",
              )
          )
  
      return request_states
  
file_path: H2O/h2o_hf/helm/src/helm/benchmark/adaptation/adapters/multiple_choice_calibrated_adapter.py
imports: from dataclasses import replace
  from typing import List
  from helm.benchmark.adaptation.request_state import RequestState
  from helm.benchmark.scenarios.scenario import Instance, Input
  from .multiple_choice_separate_adapter import MultipleChoiceSeparateAdapter
label_desc: Use image node_id 1 for calling the MultipleChoiceCalibratedAdapter obj's underlying member method code with example usage: obj.generate_requests(eval_instance) and returns: request_states