code: def stamp(
      tsA, m, tsB=None, sampling=0.2, n_threads=None, random_state=None
  ):
      """
      Calculate the Matrix Profile using the more efficient MASS calculation. Distance profiles are computed in a random order.
  
      Parameters
      ----------
      tsA: Time series containing the queries for which to calculate the Matrix Profile.
      m: Length of subsequence to compare.
      tsB: Time series to compare the query against. Note that, if no value is provided, tsB = tsA by default.
      sampling: The percentage of all possible distance profiles to sample for the final Matrix Profile. 0 to 1
      n_threads: Number of threads to use in parallel mode. Defaults to single threaded mode. Set to -1 to use all threads.
      random_state: Set the random seed generator for reproducible results.
      """
      if sampling > 1 or sampling < 0:
          raise ValueError(
              "Sampling value must be a percentage in decimal format from 0 to 1."
          )
  
      if n_threads is None:
          return _matrixProfile_sampling(
              tsA,
              m,
              order.randomOrder,
              distanceProfile.massDistanceProfile,
              tsB,
              sampling=sampling,
              random_state=random_state,
          )
  
      return _stamp_parallel(
          tsA,
          m,
          tsB=tsB,
          sampling=sampling,
          n_threads=n_threads,
          random_state=random_state,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from .None import distanceProfile
  from .None import order
  from .utils import mass, movmeanstd
  import numpy
  import multiprocessing
  from functools import partial
  import math
  from .scrimp import scrimp_plus_plus
label_desc: Use image node_id 12 for calling a global function with example usage: stamp(tsA, m, tsB, sampling, n_threads, random_state) and returns: _stamp_parallel, _matrixProfile_sampling