code: def execute(cls, ctx, op):
      (
          (
              empty_clusters,
              far_x,
              far_labels,
              far_sample_weight,
              center_new,
              weight_in_clusters,
          ),
          device_id,
          xp,
      ) = as_same_device(
          [ctx[inp.key] for inp in op.inputs], op.device, ret_extra=True
      )
  
      out_centers_new = center_new.copy()
      out_weight_in_clusters = weight_in_clusters.copy()
      del center_new, weight_in_clusters
  
      n_empty = empty_clusters.shape[0]
      n_features = far_x.shape[1]
  
      for idx in range(n_empty):
          new_cluster_id = empty_clusters[idx]
          weight = far_sample_weight[idx]
          old_cluster_id = far_labels[idx]
  
          for k in range(n_features):
              out_centers_new[old_cluster_id, k] -= (
                  far_x[idx, k] * weight
              )
              out_centers_new[new_cluster_id, k] = (
                  far_x[idx, k] * weight
              )
  
          out_weight_in_clusters[new_cluster_id] = weight
          out_weight_in_clusters[old_cluster_id] -= weight
  
      ctx[op.outputs[0].key] = out_centers_new
      ctx[op.outputs[1].key] = out_weight_in_clusters
  
imports: import numpy
  from ...None import opcodes
  from ...None import tensor
  from ...core import OutputType, recursive_tile
  from ...serialization.serializables import KeyField
  from ...tensor.array_utils import as_same_device, device, sparse
  from ...tensor.core import TensorOrder
  from ...utils import has_unknown_shape
  from ..operands import LearnOperand, LearnOperandMixin
  from ._k_means_fast import _inertia_dense, _inertia_sparse, merge_update_chunks
label_desc: Use image node_id 13 for calling the KMeansRelocateEmptyClusters obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types