docstr: Cast column of dtype object to bool.
  
  Unchecked casting to boolean when given a column of dtype object converts all non-empty cells to True. We check
  the values of the column directly and manually determine the best dtype to use.
code: def cast_column(column, backend):
      """Cast column of dtype object to bool.
  
      Unchecked casting to boolean when given a column of dtype object converts all non-empty cells to True. We check
      the values of the column directly and manually determine the best dtype to use.
      """
      values = backend.df_engine.compute(column.drop_duplicates())
  
      if strings_utils.values_are_pandas_numbers(values):
          # If numbers, convert to float so it can be converted to bool
          column = column.astype(float).astype(bool)
      elif strings_utils.values_are_pandas_bools(values):
          # If booleans, manually assign boolean values
          column = backend.df_engine.map_objects(
              column,
              lambda x: x.lower() in strings_utils.PANDAS_TRUE_STRS,
          ).astype(bool)
      else:
          # If neither numbers or booleans, they are strings (objects)
          column = column.astype(object)
      return column
  
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 BinaryFeatureMixin obj's underlying member method code with example usage: obj.cast_column(column, backend) and returns: column