code: def __init__(
      self,
      output_feature_config: Union[BinaryOutputFeatureConfig, Dict],
      output_features: Dict[str, OutputFeature],
      **kwargs,
  ):
      self.threshold = output_feature_config.threshold
      super().__init__(output_feature_config, output_features, **kwargs)
      self.decoder_obj = self.initialize_decoder(
          output_feature_config.decoder
      )
      self._setup_loss()
      self._setup_metrics()
  
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 1 to create a new BinaryOutputFeature object from inherited base classes: BinaryFeatureMixin, OutputFeature with example: obj = BinaryOutputFeature(output_feature_config, output_features)