code: def paired_cosine_distances(X, Y):
      """
      Compute the paired cosine 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)
          An array where each row is a sample and each column is a feature.
  
      Y : {array-like, sparse matrix} of shape (n_samples, n_features)
          An array where each row is a sample and each column is a feature.
  
      Returns
      -------
      distances : ndarray of shape (n_samples,)
          Returns the distances between the row vectors of `X`
          and the row vectors of `Y`, where `distances[i]` is the
          distance between `X[i]` and `Y[i]`.
  
      Notes
      -----
      The cosine distance is equivalent to the half the squared
      euclidean distance if each sample is normalized to unit norm.
  
      Examples
      --------
      >>> from sklearn.metrics.pairwise import paired_cosine_distances
      >>> X = [[0, 0, 0], [1, 1, 1]]
      >>> Y = [[1, 0, 0], [1, 1, 0]]
      >>> paired_cosine_distances(X, Y)
      array([0.5       , 0.18...])
      """
      X, Y = check_paired_arrays(X, Y)
      return 0.5 * row_norms(normalize(X) - normalize(Y), squared=True)
  
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 17 for calling a global function with example usage: paired_cosine_distances(X, Y) and returns: unknown