code: def fit(dataset, nu=0.1, save_results=True, show=False):
      
      from disco.worker.pipeline.worker import Worker, Stage
      from disco.core import Job
  
      if dataset.params["y_map"] == []:
          raise Exception("Linear proximal SVM requires a target label mapping parameter.")
      try:
          nu = float(nu)
          if nu <= 0:
              raise Exception("Parameter nu should be greater than 0")
      except ValueError:
          raise Exception("Parameter should be numerical.")
  
      job = Job(worker=Worker(save_results=save_results))
  
      # job parallelizes mappers and joins them with one reducer
      job.pipeline = [
          ("split", Stage("map", input_chain=dataset.params["input_chain"], init=simple_init, process=map_fit)),
          ('group_all', Stage("reduce", init=simple_init, process=reduce_fit, combine=True))]
  
      job.params = dataset.params
      job.params["nu"] = nu
      job.run(name="linearsvm_fit", input=dataset.params["data_tag"])
      fitmodel_url = job.wait(show=show)
      return {"linsvm_fitmodel": fitmodel_url}
docs: Function starts a job for calculation of model parameters
  
      Parameters
      ----------
      input - dataset object with input urls and other parameters
      nu - parameter to adjust the classifier
      save_results - save results to ddfs
      show - show info about job execution
  
      Returns
      -------
      Urls of fit model results on ddfs