code: def sqeuclidean_row_norms(X, num_threads):
      """Compute the squared euclidean norm of the rows of X in parallel.
  
      Parameters
      ----------
      X : ndarray or CSR matrix of shape (n_samples, n_features)
          Input data. Must be c-contiguous.
  
      num_threads : int
          The number of OpenMP threads to use.
  
      Returns
      -------
      sqeuclidean_row_norms : ndarray of shape (n_samples,)
          Arrays containing the squared euclidean norm of each row of X.
      """
      if X.dtype == np.float64:
          return np.asarray(_sqeuclidean_row_norms64(X, num_threads))
      if X.dtype == np.float32:
          return np.asarray(_sqeuclidean_row_norms32(X, num_threads))
  
      raise ValueError(
          "Only float64 or float32 datasets are supported at this time, "
          f"got: X.dtype={X.dtype}."
      )
  
imports: from abc import abstractmethod
  from typing import List
  import numpy
  from scipy.sparse import issparse
  from ...None import get_config
  from .._dist_metrics import BOOL_METRICS, METRIC_MAPPING64, DistanceMetric
  from ._argkmin import ArgKmin32, ArgKmin64
  from ._argkmin_classmode import ArgKminClassMode32, ArgKminClassMode64
  from ._base import _sqeuclidean_row_norms32, _sqeuclidean_row_norms64
  from ._radius_neighbors import RadiusNeighbors32, RadiusNeighbors64
  from ._radius_neighbors_classmode import RadiusNeighborsClassMode32, RadiusNeighborsClassMode64
label_desc: Use image node_id 1 for calling a global function with example usage: sqeuclidean_row_norms(X, num_threads) and returns: np, np