code: def _daal4py_compute_starting_centroids(
      X, X_fptype, nClusters, cluster_centers_0, random_state
  ):
      def is_string(s, target_str):
          return isinstance(s, str) and s == target_str
  
      deterministic = False
      if is_string(cluster_centers_0, "k-means++"):
          _seed = random_state.randint(np.iinfo("i").max)
          daal_engine = daal4py.engines_mt19937(
              fptype=X_fptype, method="defaultDense", seed=_seed
          )
          _n_local_trials = 2 + int(np.log(nClusters))
          kmeans_init = daal4py.kmeans_init(
              nClusters,
              fptype=X_fptype,
              nTrials=_n_local_trials,
              method="plusPlusDense",
              engine=daal_engine,
          )
          kmeans_init_res = kmeans_init.compute(X)
          centroids_ = kmeans_init_res.centroids
      elif is_string(cluster_centers_0, "random"):
          _seed = random_state.randint(np.iinfo("i").max)
          daal_engine = daal4py.engines_mt19937(
              seed=_seed, fptype=X_fptype, method="defaultDense"
          )
          kmeans_init = daal4py.kmeans_init(
              nClusters,
              fptype=X_fptype,
              method="randomDense",
              engine=daal_engine,
          )
          kmeans_init_res = kmeans_init.compute(X)
          centroids_ = kmeans_init_res.centroids
      elif hasattr(cluster_centers_0, "__array__"):
          deterministic = True
          cc_arr = np.ascontiguousarray(
              cluster_centers_0, dtype=X.dtype
          )
          _validate_center_shape(X, nClusters, cc_arr)
          centroids_ = cc_arr
      elif callable(cluster_centers_0):
          cc_arr = cluster_centers_0(X, nClusters, random_state)
          cc_arr = np.ascontiguousarray(cc_arr, dtype=X.dtype)
          _validate_center_shape(X, nClusters, cc_arr)
          centroids_ = cc_arr
      elif is_string(cluster_centers_0, "deterministic"):
          deterministic = True
          kmeans_init = daal4py.kmeans_init(
              nClusters, fptype=X_fptype, method="defaultDense"
          )
          kmeans_init_res = kmeans_init.compute(X)
          centroids_ = kmeans_init_res.centroids
      else:
          raise ValueError(
              "Cluster centers should either be 'k-means++',"
              " 'random', 'deterministic' or an array"
          )
      return deterministic, centroids_
  
imports: import numpy
  from scipy import sparse
  from sklearn.utils import check_array, check_random_state
  from sklearn.utils.sparsefuncs import mean_variance_axis
  from sklearn.utils.validation import _num_samples, check_is_fitted
  import warnings
  from sklearn.cluster import KMeans
  from sklearn.utils.extmath import row_norms
  import daal4py
  from .._device_offload import support_usm_ndarray
  from .._utils import PatchingConditionsChain, control_n_jobs, daal_check_version, getFPType, run_with_n_jobs
label_desc: Use image node_id 2 for calling a global function with example usage: _daal4py_compute_starting_centroids(X, X_fptype, nClusters, cluster_centers_0, random_state) and returns: deterministic,  centroids_, unknown