code: def _statsmodels_to_cuml(
      ref_fits, cuml_model, order, seasonal_order, intercept, dtype
  ):
      """Utility function to transfer the parameters from a statsmodels'
      SARIMAXResults object to a cuML ARIMA object.
  
      .. note:: be cautious with the intercept, it is not always equivalent
          in statsmodels and cuML models (it depends on the order).
  
      """
      nb = cuml_model.batch_size
      N = cuml_model.complexity
      x = np.zeros(nb * N, dtype=np.float64)
  
      for ib in range(nb):
          x[ib * N : (ib + 1) * N] = ref_fits[ib].params[:N]
  
      cuml_model.unpack(x)
  
imports: from cuml.testing.utils import stress_param
  from cuml.internals.input_utils import input_to_host_array
  import cuml.tsa.arima
  from cuml.internals.safe_imports import gpu_only_import
  import statsmodels.api
  from sklearn.model_selection import train_test_split
  from cuml.internals.safe_imports import cpu_only_import_from
  import warnings
  import os
  import pytest
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 7 for calling a global function with example usage: _statsmodels_to_cuml(ref_fits, cuml_model, order, seasonal_order, intercept, dtype) without return types