code: def get_feature_meta(
      config: ModelConfigDict,
      column: DataFrame,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      is_input_feature: bool,
  ) -> FeatureMetadataDict:
      if column.dtype != object:
          return {}
  
      distinct_values = backend.df_engine.compute(
          column.drop_duplicates()
      )
      if len(distinct_values) > 2:
          raise InputDataError(
              column.name,
              BINARY,
              f"expects 2 distinct values, found {distinct_values.values.tolist()}",
          )
      if preprocessing_parameters["fallback_true_label"]:
          fallback_true_label = preprocessing_parameters[
              "fallback_true_label"
          ]
      else:
          fallback_true_label = sorted(distinct_values)[0]
          preprocessing_parameters[
              "fallback_true_label"
          ] = fallback_true_label
  
      try:
          str2bool = {
              v: strings_utils.str2bool(v) for v in distinct_values
          }
      except Exception as e:
          logger.warning(
              f"Binary feature {column.name} has at least 1 unconventional boolean value: {e}. "
              f"We will now interpret {fallback_true_label} as 1 and the other values as 0. "
              f"If this is incorrect, please use the category feature type or "
              f"manually specify the true value with `preprocessing.fallback_true_label`."
          )
          str2bool = {
              v: strings_utils.str2bool(v, fallback_true_label)
              for v in distinct_values
          }
  
      bool2str = [
          k
          for k, v in sorted(str2bool.items(), key=lambda item: item[1])
      ]
      return {
          "str2bool": str2bool,
          "bool2str": bool2str,
          "fallback_true_label": fallback_true_label,
      }
  
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 3 for calling the BinaryFeatureMixin obj's underlying member method code with example usage: obj.get_feature_meta(config, column, preprocessing_parameters, backend, is_input_feature) and returns: dict, dict