docstr: Creates the appropriate calibration module based on the feature config.
  
  Today, only one type of calibration ("temperature_scaling") is available, but more options may be supported in
  the future.
code: def create_calibration_module(
      self, feature: BinaryOutputFeatureConfig
  ) -> torch.nn.Module:
      """Creates the appropriate calibration module based on the feature config.
  
      Today, only one type of calibration ("temperature_scaling") is available, but more options may be supported in
      the future.
      """
      if feature.calibration:
          calibration_cls = calibration.get_calibration_cls(
              BINARY, "temperature_scaling"
          )
          return calibration_cls(binary=True)
      return None
  
imports: import logging
  from typing import Dict, List, Tuple, Union
  import numpy
  import torch
  from ludwig.constants import BINARY, COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, PROC_COLUMN
  from ludwig.error import InputDataError
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.schema.features.binary_feature import BinaryInputFeatureConfig, BinaryOutputFeatureConfig
  from ludwig.types import FeatureConfigDict, FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import calibration, output_feature_utils, strings_utils
  from ludwig.utils.eval_utils import average_precision_score, ConfusionMatrix, precision_recall_curve, roc_auc_score, roc_curve
  from ludwig.utils.types import DataFrame, TorchscriptPreprocessingInput
label_desc: Use image node_id 3 for calling the BinaryOutputFeature obj's underlying member method code with example usage: obj.create_calibration_module(feature) and returns: None, calibration_cls