code: def minimum_satisfied(
      values: np.ndarray, min_bin_points: int, n: int
  ) -> int:
      """
      Calculates whether the partition into bins induced by `n` quantiles
      has the minimum number of points in each resulting bin.
  
      Parameters
      ----------
      values
          Array of feature values.
      min_bin_points
          Minimum number of points each discretized interval needs to contain.
      n
          Number of quantiles.
  
      Returns
      -------
      Integer encoded boolean with 1 - each bin has at least `min_bin_points` and 0 otherwise.
  
      """
      q = np.unique(get_quantiles(values, num_quantiles=n))
      indices = np.searchsorted(q, values, side="left")
      indices[indices == 0] = 1
      interval_n = np.bincount(indices)
      return int(np.all(interval_n[1:] > min_bin_points))
  
imports: import copy
  import math
  import numpy
  import pandas
  from itertools import count
  from functools import partial
  from typing import Callable, List, Optional, Tuple, Union, Dict, TYPE_CHECKING, no_type_check
  import sys
  import logging
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.api.defaults import DEFAULT_META_ALE, DEFAULT_DATA_ALE
label_desc: Use image node_id 3 for calling a global function with example usage: minimum_satisfied(values, min_bin_points, n) and returns: int