class_docstr: A sampler that uses an underlying training set to draw records that have a subset of features with
  values specified in an instance to be explained, `X`. 
class_docstr_tok: ["A","sampler","that","uses","an","underlying","training","set","to","draw","records","that","have","a","subset","of","features","with","values","specified","in","an","instance","to","be","explained",",","`","X","`","."]
docstr: Compute `min` and `max` for numerical features so that sampling from this range can be performed if
  a sampling request has bin that is not in the training data.
code: def _set_numerical_feats_stats(self) -> None:
      """
      Compute `min` and `max` for numerical features so that sampling from this range can be performed if
      a sampling request has bin that is not in the training data.
      """
  
      self.min, self.max = np.full(
          self.train_data.shape[1], np.nan
      ), np.full(self.train_data.shape[1], np.nan)
      self.min[self.numerical_features] = np.min(
          self.train_data[:, self.numerical_features], axis=0
      )
      self.max[self.numerical_features] = np.max(
          self.train_data[:, self.numerical_features], axis=0
      )
  
imports: import copy
  from collections import OrderedDict, defaultdict
  from itertools import accumulate
  from typing import Any, Callable, DefaultDict, Dict, List, Optional, Set, Tuple, Type, Union
  import numpy
  from alibi.api.defaults import DEFAULT_DATA_ANCHOR, DEFAULT_META_ANCHOR
  from alibi.api.interfaces import Explainer, Explanation, FitMixin
  from alibi.exceptions import NotFittedError, PredictorCallError, PredictorReturnTypeError
  from alibi.utils.discretizer import Discretizer
  from alibi.utils.mapping import ohe_to_ord, ord_to_ohe
  from alibi.utils.wrappers import ArgmaxTransformer
  from .anchor_base import AnchorBaseBeam
  from .anchor_explanation import AnchorExplanation
label_desc: Use image node_id 5 for calling the TabularSampler obj's underlying member method code with example usage: obj._set_numerical_feats_stats() without return types