code: def paired_manhattan_distances(X, Y):
      """Compute the paired L1 distances between X and Y.
  
      Distances are calculated between (X[0], Y[0]), (X[1], Y[1]), ...,
      (X[n_samples], Y[n_samples]).
  
      Read more in the :ref:`User Guide <metrics>`.
  
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          An array-like 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-like where each row is a sample and each column is a feature.
  
      Returns
      -------
      distances : ndarray of shape (n_samples,)
          L1 paired distances between the row vectors of `X`
          and the row vectors of `Y`.
  
      Examples
      --------
      >>> from sklearn.metrics.pairwise import paired_manhattan_distances
      >>> import numpy as np
      >>> X = np.array([[1, 1, 0], [0, 1, 0], [0, 0, 1]])
      >>> Y = np.array([[0, 1, 0], [0, 0, 1], [0, 0, 0]])
      >>> paired_manhattan_distances(X, Y)
      array([1., 2., 1.])
      """
      X, Y = check_paired_arrays(X, Y)
      diff = X - Y
      if issparse(diff):
          diff.data = np.abs(diff.data)
          return np.squeeze(np.array(diff.sum(axis=1)))
      else:
          return np.abs(diff).sum(axis=-1)
  
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 16 for calling a global function with example usage: paired_manhattan_distances(X, Y) and returns: np, np