code: def main(readcsv=pd_read_csv, *args, **kwargs):
      data_path = Path(__file__).parent / "data" / "batch"
      infile = data_path / "linear_regression_train.csv"
      testfile = data_path / "linear_regression_test.csv"
  
      # Configure a Linear regression training object for streaming
      train_algo = d4p.linear_regression_training(
          interceptFlag=True, streaming=True
      )
  
      chunk_size = 250
      lines_read = 0
      # read and feed chunk by chunk
      while True:
          # Read data in chunks
          # Let's have 10 independent, and 2 dependent variables (for each observation)
          try:
              indep_data = readcsv(
                  infile,
                  usecols=range(10),
                  skip_header=lines_read,
                  max_rows=chunk_size,
              )
              dep_data = readcsv(
                  infile,
                  usecols=range(10, 12),
                  skip_header=lines_read,
                  max_rows=chunk_size,
              )
          except Exception as e:
              if lines_read > 0:
                  break
              else:
                  raise ValueError(
                      "No training data was read - empty input file?"
                  ) from e
          # Now feed chunk
          train_algo.compute(indep_data, dep_data)
          lines_read += indep_data.shape[0]
  
      # All chunks are done, now finalize the computation
      train_result = train_algo.finalize()
  
      # Now let's do some prediction
      predict_algo = d4p.linear_regression_prediction()
      # read test data (with same #features)
      pdata = readcsv(testfile, range(10))
      ptdata = readcsv(testfile, range(10, 12))
      # now predict using the model from the training above
      predict_result = predict_algo.compute(pdata, train_result.model)
  
      # The prediction result provides prediction
      assert predict_result.prediction.shape == (
          pdata.shape[0],
          dep_data.shape[1],
      )
  
      return (train_result, predict_result, ptdata)
  
label_desc: Use image node_id 1 for calling a global function with example usage: main(readcsv) and returns: train_result,  predict_result,  ptdata