code: def main(argv=None):
      """
      This is the main function that is called when you run 'sample.py'.
  
      Here, you will load the configuration settings specified in the toml configuration
      file. It will initiate the class clientInterface which automatically connects
      to the projectServer and allows making requests, such as to get DICOM or NifTi
      data using the dataInterface contained in clientInterface.
      It will then call the function 'doRuns' to actually start doing the experiment.
      """
  
      # define the parameters that will be recognized later on to set up fileIterface
      argParser = argparse.ArgumentParser()
      argParser.add_argument(
          "--config",
          "-c",
          default=None,
          type=str,
          help="experiment config file (.json or .toml)",
      )
      argParser.add_argument(
          "--runs",
          "-r",
          default="",
          type=str,
          help="Comma separated list of run numbers",
      )
      argParser.add_argument(
          "--scans",
          "-s",
          default="",
          type=str,
          help="Comma separated list of scan number",
      )
      args = argParser.parse_args(argv)
  
      # load the experiment configuration file
      cfg = loadConfigFile(args.config)
  
      # obtain paths for important directories (e.g. location of dicom files)
      if cfg.imgDir is None:
          cfg.imgDir = os.path.join(currPath, "dicomDir")
      cfg.codeDir = currPath
  
      # Make an RPC connection to the projectServer
      # The 'dataInterface' class allows you to read and write files from the
      #   control room computer to this script running in the cloud.
      # The 'subjInterface' class will allow us to send classification results
      #   as feedback to the subject at the presentation computer.
      clientRPC = ClientInterface()
      dataInterface = clientRPC.dataInterface
      subjInterface = clientRPC.subjInterface
      webInterface = clientRPC.webInterface
  
      # now that we have the necessary variables, call the function 'doRuns' in order
      #   to actually start reading dicoms and doing your analyses of interest!
      #   INPUT:
      #       [1] cfg (configuration file with important variables)
      #       [2] dataInterface (this will allow a script from the cloud to access files
      #               from the stimulus computer that receives dicoms from the Siemens
      #               console computer)
      #       [3] subjInterface - this allows sending feedback (e.g. classification results)
      #            to a subjectService running on the presentation computer to provide
      #            feedback to the subject (and optionally get their response).
      #       [4] webInterface - this allows updating information on the experimenter webpage.
      #            For example to plot data points, or update status messages.
      doRuns(cfg, dataInterface, subjInterface, webInterface)
  
      return 0
  
file_path: brainiak/docs/examples/real-time/sample.py
imports: import os
  import sys
  import time
  import argparse
  import numpy
  import nibabel
  import scipy.io
  from nilearn.input_data import NiftiMasker
  from sklearn import preprocessing
  from sklearn import svm
  import nilearn
  from nilearn.masking import apply_mask
  from scipy.stats import zscore
  from sklearn.preprocessing import StandardScaler
  import warnings
  from rtCommon.utils import loadConfigFile
  from rtCommon.clientInterface import ClientInterface
  from rtCommon.imageHandling import readRetryDicomFromDataInterface, getDicomFileName, convertDicomImgToNifti, convertDicomFileToNifti, readNifti, convertDicomFileToNifti
label_desc: Use image node_id 2 for calling a global function with example usage: main(argv) and returns: int