code: def _execute_reduce(cls, ctx, op: "KMeansScalablePlusPlusInit"):
      from sklearn.cluster import KMeans
  
      inputs = [ctx[inp.key] for inp in op.inputs]
  
      count = np.zeros(inputs[1].shape[0], dtype=np.int64)
      for inp in inputs[1:]:
          count += inp
      weight = count / count.sum()
  
      centers = inputs[0]
  
      kmeans = KMeans(
          n_clusters=op.n_clusters, n_init=1, random_state=op.state
      )
      kmeans.fit(centers, sample_weight=weight)
      ctx[op.outputs[0].key] = kmeans.cluster_centers_
  
imports: import inspect
  import numpy
  from ...None import opcodes
  from ...None import tensor
  from ...core import OutputType, recursive_tile
  from ...core.operand import OperandStage
  from ...serialization.serializables import KeyField, Int32Field
  from ...tensor.array_utils import as_same_device, device
  from ...tensor.core import TensorOrder
  from ...tensor.random import RandomStateField
  from ...utils import has_unknown_shape
  from ..metrics import euclidean_distances
  from ..operands import LearnOperand, LearnOperandMixin
label_desc: Use image node_id 13 for calling the KMeansScalablePlusPlusInit obj's underlying member method code with example usage: obj._execute_reduce(cls, ctx, op) without return types