code: def _calcuate_topk_distances(cls, x, y, op, xp):
      metric = op.metric
      reduce_func = partial(
          cls._topk_reduce_func, topk=op.k, xp=xp, metric=op.metric
      )
      kwds = op.metric_kwargs or dict()
      need_sqrt = False
      if metric == "euclidean" and not kwds.get("squared", False):
          need_sqrt = True
          kwds["squared"] = True
      chunked_results = _pariwise_distance_chunked(
          x,
          y,
          reduce_func=reduce_func,
          metric=op.metric,
          working_memory=op.working_memory,
          xp=xp,
          **kwds,
      )
      neigh_dist, neigh_ind = zip(*chunked_results)
      dist, ind = np.vstack(neigh_dist), np.vstack(neigh_ind)
      if metric == "euclidean" and need_sqrt:
          dist = xp.sqrt(dist)
      if getattr(op, "y_offset", None) is not None:
          ind += op.y_offset
      return dist, ind
  
imports: from functools import partial
  import numpy
  from ....None import opcodes
  from ....None import options
  from ....core import recursive_tile
  from ....core.operand import OperandStage
  from ....serialization.serializables import KeyField, BoolField, DictField, Int64Field, AnyField
  from ....tensor.core import TensorOrder
  from ....tensor.merge import TensorConcatenate
  from ....tensor.array_utils import as_same_device, device, get_array_module
  from ....utils import has_unknown_shape, ensure_own_data
  from ...utils import gen_batches, get_chunk_n_rows
  from ...utils.validation import _num_samples
  from .core import PairwiseDistances
label_desc: Use image node_id 16 for calling the PairwiseDistancesTopk obj's underlying member method code with example usage: obj._calcuate_topk_distances(cls, x, y, op, xp) and returns: dist,  ind