code: def _kmeans_single_lloyd(
      X,
      sample_weight,
      centers_init,
      n_clusters,
      max_iter=300,
      verbose=False,
      x_squared_norms=None,
      tol=1e-4,
      X_mean=None,
      session=None,
      run_kwargs=None,
  ):
      sample_weight = _check_normalize_sample_weight(sample_weight, X)
  
      centers = centers_init
      # execute X, centers and tol first
      tol = mt.asarray(tol)
      to_run = [X, centers, x_squared_norms, tol]
      if X_mean is not None:
          to_run.append(X_mean)
      mt.ExecutableTuple(to_run).execute(
          session=session, **(run_kwargs or dict())
      )
      tol = tol.fetch(session=session)
  
      if verbose:  # pragma: no cover
          print("Initialization complete")
  
      labels = mt.full(X.shape[0], -1, dtype=mt.int32)
      center_shift = mt.zeros(n_clusters, dtype=X.dtype)
  
      for i in range(max_iter):
          to_runs = []
  
          (
              centers_new,
              weight_in_clusters,
              labels,
              center_shift,
          ) = lloyd_iter(
              X,
              sample_weight,
              x_squared_norms,
              centers,
              labels,
              center_shift,
              update_centers=True,
              session=session,
              run_kwargs=run_kwargs,
          )
          to_runs.extend(
              [centers_new, weight_in_clusters, labels, center_shift]
          )
  
          if verbose:
              inertia = _inertia(X, sample_weight, centers, labels)
              to_runs.append(inertia)
  
          center_shift_tot = (center_shift**2).sum()
          to_runs.append(center_shift_tot)
  
          mt.ExecutableTuple(to_runs).execute(
              session=session, **(run_kwargs or dict())
          )
  
          if verbose:  # pragma: no cover
              inertia_data = inertia.fetch(session=session)
              print(f"Iteration {i}, inertia {inertia_data}")
  
          center_shift_tot = center_shift_tot.fetch(session=session)
          if center_shift_tot <= tol:
              if verbose:  # pragma: no cover
                  print(
                      f"Converged at iteration {i}: center shift {center_shift_tot} "
                      f"within tolerance {tol}"
                  )
              break
  
          centers, centers_new = centers_new, centers
  
      if center_shift_tot > 0:
          # rerun E-step so that predicted labels match cluster centers
          (
              centers_new,
              weight_in_clusters,
              labels,
              center_shift,
          ) = lloyd_iter(
              X,
              sample_weight,
              x_squared_norms,
              centers,
              labels,
              center_shift,
              update_centers=False,
              session=session,
              run_kwargs=run_kwargs,
          )
  
      inertia = _inertia(X, sample_weight, centers, labels)
  
      mt.ExecutableTuple([labels, inertia, centers]).execute(
          session=session, **(run_kwargs or dict())
      )
      return labels, inertia, centers, i + 1
  
imports: import warnings
  import numpy
  from sklearn.base import ClusterMixin, TransformerMixin
  from sklearn.exceptions import ConvergenceWarning
  from ...None import tensor
  from ...tensor.utils import check_random_state
  from ..base import BaseEstimator
  from ..metrics.pairwise import euclidean_distances
  from ..utils.extmath import row_norms
  from ..utils.validation import _check_sample_weight, check_array, _num_samples, check_is_fitted
  from ._k_means_common import _inertia
  from ._k_means_elkan_iter import init_bounds, elkan_iter
  from ._k_means_init import _k_init, _scalable_k_init
  from ._k_means_lloyd_iter import lloyd_iter
label_desc: Use image node_id 6 for calling a global function with example usage: _kmeans_single_lloyd(X, sample_weight, centers_init, n_clusters, max_iter, verbose, x_squared_norms, tol, X_mean, session, run_kwargs) and returns: labels,  inertia,  centers,  unknown