code: def paired_euclidean_distances(X, Y):
      """Compute the paired euclidean distances between X and Y.
  
      Read more in the :ref:`User Guide <metrics>`.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          Input array/matrix X.
  
      Y : {array-like, sparse matrix} of shape (n_samples, n_features)
          Input array/matrix Y.
  
      Returns
      -------
      distances : ndarray of shape (n_samples,)
          Output array/matrix containing the calculated paired euclidean
          distances.
  
      Examples
      --------
      >>> from sklearn.metrics.pairwise import paired_euclidean_distances
      >>> X = [[0, 0, 0], [1, 1, 1]]
      >>> Y = [[1, 0, 0], [1, 1, 0]]
      >>> paired_euclidean_distances(X, Y)
      array([1., 1.])
      """
      X, Y = check_paired_arrays(X, Y)
      return row_norms(X - Y)
  
imports: import itertools
  import warnings
  from functools import partial
  from numbers import Integral, Real
  import numpy
  from joblib import effective_n_jobs
  from scipy.sparse import csr_matrix, issparse
  from scipy.spatial import distance
  from ..None import config_context
  from ..exceptions import DataConversionWarning
  from ..preprocessing import normalize
  from ..utils import check_array, gen_batches, gen_even_slices, get_chunk_n_rows, is_scalar_nan
  from ..utils._mask import _get_mask
  from ..utils._param_validation import Hidden, Interval, MissingValues, Options, StrOptions, validate_params
  from ..utils.extmath import row_norms, safe_sparse_dot
  from ..utils.fixes import parse_version, sp_base_version
  from ..utils.parallel import Parallel, delayed
  from ..utils.validation import _num_samples, check_non_negative
  from ._pairwise_distances_reduction import ArgKmin
  from ._pairwise_fast import _chi2_kernel_fast, _sparse_manhattan
label_desc: Use image node_id 15 for calling a global function with example usage: paired_euclidean_distances(X, Y) and returns: row_norms