docstr: The extract features function for dask returns a
  dataframe of [id, variable, value].
  Turn this into a pivoted dataframe, where only the variables are the columns
  and the ids are the rows.
  
  Attention: this is highly non-optimized!
code: def pivot(self, results):
      """
      The extract features function for dask returns a
      dataframe of [id, variable, value].
      Turn this into a pivoted dataframe, where only the variables are the columns
      and the ids are the rows.
  
      Attention: this is highly non-optimized!
      """
      results = results.reset_index(drop=True).persist()
      results = results.categorize(columns=["variable"])
      feature_table = results.pivot_table(
          index=self.column_id,
          columns="variable",
          values="value",
          aggfunc="sum",
      )
  
      return feature_table
  
imports: import itertools
  from collections import defaultdict, namedtuple
  from typing import Iterable, Sized
  import pandas
label_desc: Use image node_id 3 for calling the DaskTsAdapter obj's underlying member method code with example usage: obj.pivot(results) and returns: feature_table