code: def __init__(
      self,
      input_feature_config: BinaryInputFeatureConfig,
      encoder_obj=None,
      **kwargs,
  ):
      super().__init__(input_feature_config, **kwargs)
      input_feature_config.encoder.input_size = self.input_shape[-1]
  
      if encoder_obj:
          self.encoder_obj = encoder_obj
      else:
          self.encoder_obj = self.initialize_encoder(
              input_feature_config.encoder
          )
  
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 BinaryInputFeature object from inherited base classes: BinaryFeatureMixin, InputFeature with example: obj = BinaryInputFeature(input_feature_config, encoder_obj)