docstr: Search for the k nearest neighbors of a query vector q.
  
  Parameters:
  - q: the query vector to search for
  - k: the number of nearest neighbors to return (default: 5)
  
  Returns:
  - Index: a numpy array of size (k,) containing the indices of the k nearest neighbors
  - Distances: a numpy array of size (k,) containing the distances to the k nearest neighbors
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code: def search(self, q, k=5):
      """
      Search for the k nearest neighbors of a query vector q.
  
      Parameters:
      - q: the query vector to search for
      - k: the number of nearest neighbors to return (default: 5)
  
      Returns:
      - Index: a numpy array of size (k,) containing the indices of the k nearest neighbors
      - Distances: a numpy array of size (k,) containing the distances to the k nearest neighbors
      """
      q = np.asarray(q, dtype=np.float32)
      Distances, Index = self.index.search(q.reshape(1, -1), k)
      return Index[0], Distances[0]
  
file_path: pygraphistry/graphistry/ai_utils.py
imports: import pandas
  import numpy
  import graphistry
  from .constants import DISTANCE, WEIGHT, BATCH
  from logging import getLogger
label_desc: Use image node_id 2 for calling the FaissVectorSearch obj's underlying member method code with example usage: obj.search(q, k) and returns: Index,  Distances