code: def forward(
      self, preds: Dict[str, torch.Tensor], feature_name: str
  ) -> FeaturePostProcessingOutputDict:
      predictions = output_feature_utils.get_output_feature_tensor(
          preds, feature_name, self.predictions_key
      )
      probabilities = output_feature_utils.get_output_feature_tensor(
          preds, feature_name, self.probabilities_key
      )
  
      if self.bool2str is not None:
          predictions = predictions.to(dtype=torch.int32)
          predictions = [
              self.bool2str.get(pred, self.bool2str[0])
              for pred in predictions
          ]
  
      probabilities = torch.stack(
          [1 - probabilities, probabilities], dim=-1
      )
  
      return {
          self.predictions_key: predictions,
          self.probabilities_key: probabilities,
      }
  
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 2 for calling the _BinaryPostprocessing obj's underlying member method code with example usage: obj.forward(preds, feature_name) and returns: dict