code: def forward(
      self, inputs: Dict[str, torch.Tensor], feature_name: str
  ) -> Dict[str, torch.Tensor]:
      logits = output_feature_utils.get_output_feature_tensor(
          inputs, feature_name, self.logits_key
      )
  
      if self.calibration_module is not None:
          probabilities = self.calibration_module(logits)
      else:
          probabilities = torch.sigmoid(logits)
  
      predictions = probabilities >= self.threshold
      return {
          self.probabilities_key: probabilities,
          self.predictions_key: predictions,
          self.logits_key: logits,
      }
  
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 _BinaryPredict obj's underlying member method code with example usage: obj.forward(inputs, feature_name) and returns: dict