code: def postprocess_predictions(
      self,
      result,
      metadata,
  ):
      class_names = ["False", "True"]
      if "bool2str" in metadata:
          class_names = metadata["bool2str"]
  
      predictions_col = f"{self.feature_name}_{PREDICTIONS}"
      if predictions_col in result:
          if "bool2str" in metadata:
              result[predictions_col] = result[predictions_col].map(
                  lambda pred: metadata["bool2str"][pred],
              )
  
      probabilities_col = f"{self.feature_name}_{PROBABILITIES}"
      if probabilities_col in result:
          false_col = f"{probabilities_col}_{class_names[0]}"
          true_col = f"{probabilities_col}_{class_names[1]}"
          prob_col = f"{self.feature_name}_{PROBABILITY}"
  
          result = result.assign(
              **{
                  false_col: lambda x: 1 - x[probabilities_col],
                  true_col: lambda x: x[probabilities_col],
                  prob_col: np.where(
                      result[probabilities_col] > 0.5,
                      result[probabilities_col],
                      1 - result[probabilities_col],
                  ),
                  probabilities_col: result[probabilities_col].map(
                      lambda x: [1 - x, x]
                  ),
              },
          )
  
      return result
  
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 11 for calling the BinaryOutputFeature obj's underlying member method code with example usage: obj.postprocess_predictions(result, metadata) and returns: result