code: def gen_data_by_type(field):
      data_type = field["type"]
      if data_type == "bool":
          return random.choice([True, False])
      if data_type == "int8":
          return random.randint(-128, 127)
      if data_type == "int16":
          return random.randint(-32768, 32767)
      if data_type == "int32":
          return random.randint(-2147483648, 2147483647)
      if data_type == "int64":
          return random.randint(
              -9223372036854775808, 9223372036854775807
          )
      if data_type == "float32":
          return np.float64(
              random.random()
          )  # Object of type float32 is not JSON serializable, so set it as float64
      if data_type == "float64":
          return np.float64(random.random())
      if "varchar" in data_type:
          length = int(data_type.split("(")[1].split(")")[0])
          return "".join(
              [chr(random.randint(97, 122)) for _ in range(length)]
          )
      if "floatVector" in data_type:
          dim = int(data_type.split("(")[1].split(")")[0])
          return preprocessing.normalize(
              [np.array([random.random() for i in range(dim)])]
          )[0].tolist()
      return None
  
imports: import random
  import time
  import random
  import string
  from faker import Faker
  import numpy
  from sklearn import preprocessing
  import requests
  from loguru import logger
  import datetime
label_desc: Use image node_id 6 for calling a global function with example usage: gen_data_by_type(field) and returns: None, random, random, random, random, random, np, np, str, preprocessing