code: def get_vectors_from_binary(nq, dimension, data_type):
      # use the first file, nq should be less than VECTORS_PER_FILE 10001
      if nq > MAX_NQ:
          raise Exception("Over size nq")
      if data_type == "local":
          return generate_vectors(nq, dimension)
      elif data_type == "random":
          file_name = RANDOM_SRC_DATA_DIR + "query_%d.npy" % dimension
      elif data_type == "sift":
          file_name = SIFT_SRC_DATA_DIR + "query.npy"
      elif data_type == "deep":
          file_name = DEEP_SRC_DATA_DIR + "query.npy"
      elif data_type == "binary":
          file_name = BINARY_SRC_DATA_DIR + "query.npy"
      else:
          raise Exception(
              "There is no corresponding file for this data type %s."
              % str(data_type)
          )
      data = np.load(file_name)
      vectors = data[0:nq].tolist()
      return vectors
  
imports: import os
  import logging
  import numpy
  import sklearn.preprocessing
  import h5py
  import random
  from itertools import product
  from pymilvus import DataType
  from milvus_benchmark import config
label_desc: Use image node_id 2 for calling a global function with example usage: get_vectors_from_binary(nq, dimension, data_type) and returns: vectors, generate_vectors