code: def compute(
      train_indep_data,
      train_dep_data,
      test_indep_data,
      method="defaultDense",
  ):
      # Configure a SVM object to use linear kernel
      kernel_function = d4p.kernel_function_linear(
          fptype="float", method="defaultDense", k=1.0, b=0.0
      )
      train_algo = d4p.svm_training(
          fptype="float",
          method=method,
          kernel=kernel_function,
          C=1.0,
          accuracyThreshold=1e-3,
          tau=1e-8,
          cacheSize=600000000,
      )
  
      train_result = train_algo.compute(
          train_indep_data, train_dep_data
      )
  
      # Create an algorithm object and call compute
      predict_algo = d4p.svm_prediction(
          fptype="float", kernel=kernel_function
      )
      predict_result = predict_algo.compute(
          test_indep_data, train_result.model
      )
      decision_result = predict_result.prediction
      predict_labels = np.where(decision_result >= 0, 1, -1)
      return predict_labels, decision_result
  
imports: import os
  import numpy
  import daal4py
  from daal4py.oneapi import sycl_buffer
label_desc: Use image node_id 1 for calling a global function with example usage: compute(train_indep_data, train_dep_data, test_indep_data, method) and returns: predict_labels,  decision_result