code: def main(readcsv=read_csv, method="defaultDense"):
      # read data from file
      file = os.path.join(
          "..",
          "..",
          "..",
          "examples",
          "daal4py",
          "data",
          "batch",
          "covcormoments_dense.csv",
      )
      data = readcsv(file, range(10), t=np.float32)
  
      # Using of the classic way (computations on CPU)
      result_classic = compute(data, method)
  
      data = to_numpy(data)
  
      # It is possible to specify to make the computations on GPU
      if gpu_available:
          with sycl_context("gpu"):
              sycl_data = sycl_buffer(data)
              result_gpu = compute(sycl_data, "defaultDense")
          for name in [
              "minimum",
              "maximum",
              "sum",
              "sumSquares",
              "sumSquaresCentered",
              "mean",
              "secondOrderRawMoment",
              "variance",
              "standardDeviation",
              "variation",
          ]:
              assert np.allclose(
                  getattr(result_classic, name),
                  getattr(result_gpu, name),
              )
  
      # result provides minimum, maximum, sum, sumSquares, sumSquaresCentered,
      # mean, secondOrderRawMoment, variance, standardDeviation, variation
      assert all(
          getattr(result_classic, name).shape == (1, data.shape[1])
          for name in [
              "minimum",
              "maximum",
              "sum",
              "sumSquares",
              "sumSquaresCentered",
              "mean",
              "secondOrderRawMoment",
              "variance",
              "standardDeviation",
              "variation",
          ]
      )
  
      return result_classic
  
imports: import os
  import numpy
  import daal4py
  from daal4py.oneapi import sycl_buffer
label_desc: Use image node_id 3 for calling a global function with example usage: main(readcsv, method) and returns: result_classic