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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def runExperimentS(dirName):
 """ This runs an experiment where the network is trained on stream containing a mixture of temporal and sensorimotor sequences. """

  # Results are put into a pkl file which can be used to generate the plots.
  # dirName is the absolute path where the pkl file will be placed.
  resultsFilename = os.path.join(dirName, "superimposed_training.pkl")
  results = runExperiment(
    {
      "numSequences": 50,
      "numObjects": 50,
      "seqLength": 10,
      "numFeatures": 100,
      "trialNum": 8,
      "numLocations": 100,
      "numRepetitions": 30,
      "sampleSizeProximalL2": 15,
      "minThresholdProximalL2": 10,
      "figure": "S",
      "stripStats": False,
    }
  )

  # Pickle results for plotting and possible later debugging
  with open(resultsFilename, "wb") as f:
    cPickle.dump(results, f)

  # Debugging
  with open(resultsFilename, "rb") as f:
    r = cPickle.load(f)

    r.pop("objects")
    r.pop("sequences")
    stat = r.pop("statistics")
    pprint.pprint(r)
    sObject = 0
    sSequence = 0
    for i in range(0, 50):
      sObject += sum(stat[i]['L4 PredictedActive C0'])
    for i in range(50, 100):
      sSequence += sum(stat[i]['L4 PredictedActive C0'])
    print sObject, sSequence