code: def _mean_shift_single_seed(my_mean, X, nbrs, max_iter):
      # For each seed, climb gradient until convergence or max_iter
      bandwidth = nbrs.get_params()["radius"]
      stop_thresh = 1e-3 * bandwidth  # when mean has converged
      completed_iterations = 0
      while True:
          # Find mean of points within bandwidth
          i_nbrs = nbrs.radius_neighbors(
              [my_mean], bandwidth, return_distance=False
          )[0]
          points_within = X[i_nbrs]
          if len(points_within) == 0:
              break  # Depending on seeding strategy this condition may occur
          my_old_mean = my_mean  # save the old mean
          my_mean = np.mean(points_within, axis=0)
          # If converged or at max_iter, adds the cluster
          if (
              np.linalg.norm(my_mean - my_old_mean) < stop_thresh
              or completed_iterations == max_iter
          ):
              break
          completed_iterations += 1
      return tuple(my_mean), len(points_within), completed_iterations
  
imports: import warnings
  from collections import defaultdict
  from numbers import Integral, Real
  import numpy
  from .._config import config_context
  from ..base import BaseEstimator, ClusterMixin, _fit_context
  from ..metrics.pairwise import pairwise_distances_argmin
  from ..neighbors import NearestNeighbors
  from ..utils import check_array, check_random_state, gen_batches
  from ..utils._param_validation import Interval, validate_params
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling a global function with example usage: _mean_shift_single_seed(my_mean, X, nbrs, max_iter) and returns: tuple,  len,  completed_iterations