code: def _transform(self, df: DataFrame) -> DataFrame:
      dot = _make_dot()
  
      tenant_col = self.tenant_col
      user_col = self.user_col
      user_vec_col = self.user_vec_col
      res_col = self.res_col
      res_vec_col = self.res_vec_col
      output_col = self.output_col
  
      seen_token = "__seen__"
  
      def value_calc():
          return f.when(
              f.col(seen_token).isNull() | ~f.col(seen_token),
              f.when(
                  f.col(user_vec_col).isNotNull()
                  & f.col(res_vec_col).isNotNull(),
                  f.when(
                      f.col("user_component") == f.col("res_component"),
                      dot(f.col(user_vec_col), f.col(res_vec_col)),
                  ).otherwise(f.lit(float("inf"))),
              ).otherwise(f.lit(None))
              if self.has_components
              else f.when(
                  f.col(user_vec_col).isNotNull()
                  & f.col(res_vec_col).isNotNull(),
                  dot(f.col(user_vec_col), f.col(res_vec_col)),
              ).otherwise(f.lit(None)),
          ).otherwise(f.lit(0.0))
  
      history_access_df = (
          self.user_res_feature_vector_mapping.history_access_df
      )
  
      the_df = (
          df.join(
              history_access_df.withColumn(seen_token, f.lit(True)),
              [tenant_col, user_col, res_col],
              how="left",
          )
          if self.preserve_history and history_access_df is not None
          else df.withColumn(seen_token, f.lit(False))
      )
  
      user_mapping_df = self.user_mapping_df
      res_mapping_df = self.res_mapping_df
  
      return (
          the_df.join(
              user_mapping_df, [tenant_col, user_col], how="left"
          )
          .join(res_mapping_df, [tenant_col, res_col], how="left")
          .withColumn(output_col, value_calc())
          .drop(
              user_vec_col,
              res_vec_col,
              "user_component",
              "res_component",
              seen_token,
          )
      )
  
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 12 for calling the AccessAnomalyModel obj's underlying member method code with example usage: obj._transform(df) and returns: the_df, f