code: def boss_distance(X, Y, i, XX_all=None, XY_all=None):
      """Find the distance between two histograms.
  
      This returns the distance between first and second dictionaries, using a non-
      symmetric distance measure. It is used to find the distance between histograms
      of words.
  
      This distance function is designed for sparse matrix, represented as either a
      dictionary or an array. It only measures the distance between counts present in
      the first dictionary and the second. Hence dist(a,b) does not necessarily equal
      dist(b,a).
  
      Parameters
      ----------
      X : sparse matrix
          Base dictionary used in distance measurement.
      Y : sparse matrix
          Second dictionary that will be used to measure distance from `first`.
      i : int
          index of current element
  
      Returns
      -------
      dist : float
          The boss distance between the first and second dictionaries.
      """
      mask = X[i].nonzero()[1]
      if XX_all is None:
          XX = csr_row_norms(X[i])
      else:
          XX = XX_all[i]
      if XY_all is None:
          XY = safe_sparse_dot(X[i], Y.T, dense_output=True)
      else:
          XY = XY_all[i]
  
      YY = csr_row_norms(Y[:, mask])
      A = XX - 2 * XY + YY
      np.maximum(A, 0, out=A)
      return A
  
imports: from copy import copy
  from itertools import compress
  import numpy
  from joblib import Parallel, effective_n_jobs
  from sklearn.metrics import pairwise
  from sklearn.utils import check_random_state, gen_even_slices
  from sklearn.utils.extmath import safe_sparse_dot
  from sklearn.utils.fixes import delayed
  from sklearn.utils.sparsefuncs_fast import csr_row_norms
  from sklearn.utils.validation import _num_samples
  from sktime.classification.base import BaseClassifier
  from sktime.transformations.panel.dictionary_based import SFAFast
  from sktime.utils.validation.panel import check_X_y
label_desc: Use image node_id 3 for calling a global function with example usage: boss_distance(X, Y, i, XX_all, XY_all) and returns: A