class_docstr: Tabular sampling post-processing. Given the output of the heterogeneous auto-encoder the post-processing
  functions samples the output according to the conditional vector. Note that the original input instance
  is required to perform the conditional sampling.
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docstr: Constructor.
  
  Parameters
  ----------
  category_map
      Dictionary of category mapping. The keys are column indexes and the values are lists containing the
      possible feature values.
  stats
      Dictionary of statistic of the training data. Contains the minimum and maximum value of each numerical
      feature in the training set. Each key is an index of the column and each value is another dictionary
      containing ``'min'`` and ``'max'`` keys.
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code: def __init__(
      self,
      category_map: Dict[int, List[str]],
      stats: Dict[int, Dict[str, float]],
  ):
      """
      Constructor.
  
      Parameters
      ----------
      category_map
          Dictionary of category mapping. The keys are column indexes and the values are lists containing the
          possible feature values.
      stats
          Dictionary of statistic of the training data. Contains the minimum and maximum value of each numerical
          feature in the training set. Each key is an index of the column and each value is another dictionary
          containing ``'min'`` and ``'max'`` keys.
      """
      super().__init__()
      self.category_map = category_map
      self.stats = stats
  
imports: from functools import partial
  from itertools import count
  from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union, cast
  import numpy
  from tqdm import tqdm
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.explainers.backends.cfrl_tabular import get_conditional_vector, get_statistics, sample
  from alibi.explainers.cfrl_base import _PARAM_TYPES, CounterfactualRL, Postprocessing
  from alibi.utils.frameworks import has_pytorch, has_tensorflow
label_desc: Use image node_id 1 to create a new SampleTabularPostprocessing object from inherited base classes: Postprocessing with example: obj = SampleTabularPostprocessing(category_map, stats)