code: def kl_divergence_bh(
      embedding,
      P,
      dof,
      bh_params,
      reference_embedding=None,
      should_eval_error=False,
      n_jobs=1,
      **_,
  ):
      gradient = np.zeros_like(embedding, dtype=np.float64, order="C")
  
      # In the event that we wish to embed new points into an existing embedding
      # using simple optimization, we compute optimize the new embedding points
      # w.r.t. the existing embedding. Otherwise, we want to optimize the
      # embedding w.r.t. itself. We've also got to make sure that the points'
      # interactions don't interfere with each other
      pairwise_normalization = reference_embedding is None
      if reference_embedding is None:
          reference_embedding = embedding
  
      # Compute negative gradient
      tree = QuadTree(reference_embedding)
      sum_Q = _tsne.estimate_negative_gradient_bh(
          tree,
          embedding,
          gradient,
          **bh_params,
          dof=dof,
          num_threads=n_jobs,
          pairwise_normalization=pairwise_normalization,
      )
      del tree
  
      # Compute positive gradient
      sum_P, kl_divergence_ = _tsne.estimate_positive_gradient_nn(
          P.indices,
          P.indptr,
          P.data,
          embedding,
          reference_embedding,
          gradient,
          dof,
          num_threads=n_jobs,
          should_eval_error=should_eval_error,
      )
  
      # Computing positive gradients summed up only unnormalized q_ijs, so we
      # have to include normalziation term separately
      if should_eval_error:
          kl_divergence_ += sum_P * np.log(sum_Q + EPSILON)
  
      return kl_divergence_, gradient
  
imports: import inspect
  import logging
  import multiprocessing
  from collections.abc import Iterable
  from types import SimpleNamespace
  from time import time
  import numpy
  from sklearn.base import BaseEstimator
  from openTSNE import _tsne
  from openTSNE import initialization
  from openTSNE.affinity import Affinities, MultiscaleMixture
  from openTSNE.quad_tree import QuadTree
  from openTSNE import utils
label_desc: Use image node_id 5 for calling a global function with example usage: kl_divergence_bh(embedding, P, dof, bh_params, reference_embedding, should_eval_error, n_jobs) and returns: kl_divergence_,  gradient