code: def __init__(
      self,
      empty_clusters=None,
      far_x=None,
      far_labels=None,
      far_sample_weights=None,
      centers_new=None,
      weight_in_clusters=None,
      output_types=None,
      **kw,
  ):
      super().__init__(
          _empty_clusters=empty_clusters,
          _far_x=far_x,
          _far_labels=far_labels,
          _far_sample_weights=far_sample_weights,
          _centers_new=centers_new,
          _weight_in_clusters=weight_in_clusters,
          _output_types=output_types,
          **kw,
      )
      if self._output_types is None:
          self._output_types = [OutputType.tensor] * self.output_limit
  
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 1 to create a new KMeansRelocateEmptyClusters object from inherited base classes: LearnOperand, LearnOperandMixin with example: obj = KMeansRelocateEmptyClusters(empty_clusters, far_x, far_labels, far_sample_weights, centers_new, weight_in_clusters, output_types)