code: def __call__(
      self,
      query,
      k,
      reference=None,
      ref_includes_query=False,
  ):
      if ref_includes_query:
          k = k + 1
      device = query.device
      is_cuda = query.is_cuda
      d = query.shape[1]
      c_f.LOGGER.info("running k-nn with k=%d" % k)
      c_f.LOGGER.info("embedding dimensionality is %d" % d)
      if self.reset_before:
          self.index = self.index_init_fn(d)
      if self.index is None:
          raise ValueError(
              "self.index is None. It needs to be initialized before being used."
          )
      distances, indices = try_gpu(
          self.index,
          query,
          reference,
          k,
          is_cuda,
          self.gpus,
      )
      distances = c_f.to_device(distances, device=device)
      indices = c_f.to_device(indices, device=device)
      if self.reset_after:
          self.reset()
      return return_results(distances, indices, ref_includes_query)
  
imports: import numpy
  import torch
  from ..distances import BatchedDistance, CosineSimilarity
  from .None import common_functions
label_desc: Use image node_id 2 for calling the FaissKNN obj's underlying member method code with example usage: obj.__call__(query, k, reference, ref_includes_query) and returns: return_results