code: def histogram_intersection(first, second):
      """Find the distance between two histograms using the histogram intersection.
  
      This distance function is designed for sparse matrix, represented as a
      dictionary or numba Dict, but can accept arrays.
  
      Parameters
      ----------
      first : dict, numba.Dict or array
          First dictionary used in distance measurement.
      second : dict, numba.Dict or array
          Second dictionary that will be used to measure distance from `first`.
  
      Returns
      -------
      dist : float
          The histogram intersection distance between the first and second dictionaries.
      """
      from numba.typed import Dict
  
      from sktime.classification.dictionary_based._tde_numba import (
          _histogram_intersection_dict,
      )
  
      if isinstance(first, dict):
          sim = 0
          for word, val_a in first.items():
              val_b = second.get(word, 0)
              sim += min(val_a, val_b)
          return sim
      elif isinstance(first, Dict):
          return _histogram_intersection_dict(first, second)
      else:
          return np.sum(
              [
                  0 if first[n] == 0 else np.min(first[n], second[n])
                  for n in range(len(first))
              ]
          )
  
imports: import math
  import time
  from collections import defaultdict
  import numpy
  from joblib import Parallel, delayed
  from sklearn import preprocessing
  from sklearn.kernel_ridge import KernelRidge
  from sklearn.utils import check_random_state
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.dictionary_based import SFA
  from sktime.utils.validation.panel import check_X_y
  from sktime.utils.warnings import warn
label_desc: Use image node_id 1 for calling a global function with example usage: histogram_intersection(first, second) and returns: sim, _histogram_intersection_dict, np