code: def knn(xq, xb, k, metric=METRIC_L2):
      """
      Compute the k nearest neighbors of a vector without constructing an index
  
  
      Parameters
      ----------
      xq : array_like
          Query vectors, shape (nq, d) where the dimension d is that same as xb
          `dtype` must be float32.
      xb : array_like
          Database vectors, shape (nb, d) where dimension d is the same as xq
          `dtype` must be float32.
      k : int
          Number of nearest neighbors.
      distance_type : MetricType, optional
          distance measure to use (either METRIC_L2 or METRIC_INNER_PRODUCT)
  
      Returns
      -------
      D : array_like
          Distances of the nearest neighbors, shape (nq, k)
      I : array_like
          Labels of the nearest neighbors, shape (nq, k)
      """
      xq = np.ascontiguousarray(xq, dtype="float32")
      xb = np.ascontiguousarray(xb, dtype="float32")
      nq, d = xq.shape
      nb, d2 = xb.shape
      assert d == d2
  
      I = np.empty((nq, k), dtype="int64")
      D = np.empty((nq, k), dtype="float32")
  
      if metric == METRIC_L2:
          knn_L2sqr(
              swig_ptr(xq),
              swig_ptr(xb),
              d,
              nq,
              nb,
              k,
              swig_ptr(D),
              swig_ptr(I),
          )
      elif metric == METRIC_INNER_PRODUCT:
          knn_inner_product(
              swig_ptr(xq),
              swig_ptr(xb),
              d,
              nq,
              nb,
              k,
              swig_ptr(D),
              swig_ptr(I),
          )
      else:
          raise NotImplementedError(
              "only L2 and INNER_PRODUCT are supported"
          )
      return D, I
  
imports: import numpy
  from faiss.loader import *
  import faiss
  import collections.abc
label_desc: Use image node_id 14 for calling a global function with example usage: knn(xq, xb, k, metric) and returns: D,  I