code: def get_feature_range_mask(
      X, filler_feature_values=None, filler_feature_ranges=None
  ):
      """
      Function that constucts a boolean array to get rid of samples
      in X that are outside the feature range specified by filler_feature_values
      and filler_feature_ranges
      """
  
      if not isinstance(X, np.ndarray) or not len(X.shape) == 2:
          raise ValueError("X must be a 2D array")
      elif filler_feature_values is None:
          raise ValueError("filler_feature_values must not be None")
      elif filler_feature_ranges is None:
          raise ValueError("filler_feature_ranges must not be None")
  
      mask = np.ones(X.shape[0], dtype=bool)
      for feature_idx in filler_feature_ranges:
          feature_value = filler_feature_values[feature_idx]
          feature_width = filler_feature_ranges[feature_idx]
          upp_limit = feature_value + feature_width
          low_limit = feature_value - feature_width
          feature_mask = (X[:, feature_idx] > low_limit) & (
              X[:, feature_idx] < upp_limit
          )
          mask = mask & feature_mask
  
      return mask
  
file_path: mlxtend/mlxtend/plotting/decision_regions.py
imports: import multiprocessing
  from itertools import cycle
  from math import ceil, floor
  import matplotlib.pyplot
  import numpy
  from mlxtend.utils import check_Xy, format_kwarg_dictionaries
label_desc: Use image node_id 1 for calling a global function with example usage: get_feature_range_mask(X, filler_feature_values, filler_feature_ranges) and returns: mask