code: def _execute_merge_update(ctx, op):
      inputs, device_id, xp = as_same_device(
          [ctx[inp.key] for inp in op.inputs],
          op.device,
          ret_extra=True,
          copy_if_not_writeable=True,
      )
      length = len(inputs) // 2
      assert len(inputs) % 2 == 0
      centers_new_chunks = inputs[:length]
      weight_in_cluster_chunks = inputs[length:]
  
      with device(device_id):
          weight_in_clusters = np.zeros(
              op.n_clusters, dtype=weight_in_cluster_chunks[0].dtype
          )
          centers_new = np.zeros_like(centers_new_chunks[0])
          n_clusters = op.n_clusters
          n_features = centers_new_chunks[0].shape[1]
  
          for weight_in_clusters_chunk, centers_new_chunk in zip(
              weight_in_cluster_chunks, centers_new_chunks
          ):
              merge_update_chunks(
                  n_clusters,
                  n_features,
                  weight_in_clusters,
                  weight_in_clusters_chunk,
                  centers_new,
                  centers_new_chunk,
              )
  
          # centers new
          ctx[op.outputs[0].key] = centers_new
          # weight_in_clusters
          ctx[op.outputs[1].key] = 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 2 for calling a global function with example usage: _execute_merge_update(ctx, op) without return types