code: def _make_dot():
      """
      create a method that performs a dot product between two vectors (list of doubles)
      :return: the method
      """
  
      @f.udf(t.DoubleType())
      def dot(v, u):
          if (v is not None) and (u is not None):
              vv = (
                  np.pad(
                      np.array(v),
                      (0, len(u) - len(v)),
                      "constant",
                      constant_values=1.0,
                  )
                  if len(v) < len(u)
                  else np.array(v)
              )
              uu = (
                  np.pad(
                      np.array(u),
                      (0, len(v) - len(u)),
                      "constant",
                      constant_values=1.0,
                  )
                  if len(u) < len(v)
                  else np.array(u)
              )
  
              return float(vv.dot(uu))
          else:
              return None
  
      return dot
  
imports: import os
  from typing import List, Optional, Tuple
  from synapse.ml.cyber.anomaly.complement_access import ComplementAccessTransformer
  from synapse.ml.cyber.feature import indexers, scalers
  from synapse.ml.cyber.utils import spark_utils
  import numpy
  from pyspark import SQLContext
  from pyspark.ml import Estimator, Transformer
  from pyspark.ml.param.shared import Param, Params
  from pyspark.ml.recommendation import ALS
  from pyspark.sql import DataFrame, functions, types
label_desc: Use image node_id 1 for calling a global function with example usage: _make_dot() and returns: dot, float, None