code: def execute(cls, ctx, op):
      (
          (x, sample_weight, centers, labels),
          device_id,
          xp,
      ) = as_same_device(
          [ctx[inp.key] for inp in op.inputs],
          device=op.device,
          ret_extra=True,
          copy_if_not_writeable=True,
      )
  
      with device(device_id):
          if xp is np:
              method = _inertia_dense
          elif xp is sparse:
              method = _inertia_sparse
          else:  # pragma: no cover
              raise NotImplementedError("Cannot run inertial on GPU")
  
          result = method(x, sample_weight, centers, labels)
          ctx[op.outputs[0].key] = np.array([result])
  
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 9 for calling the KMeansInertia obj's underlying member method code with example usage: obj.execute(cls, ctx, op) without return types