code: def paired_manhattan_distances(X, Y):
      """Compute the L1 distances between the vectors in X and Y.
  
      Read more in the :ref:`User Guide <metrics>`.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
  
      Y : array-like, shape (n_samples, n_features)
  
      Returns
      -------
      distances : ndarray (n_samples, )
      """
      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 numpy
  from scipy.spatial import distance
  from scipy.sparse import csr_matrix
  from scipy.sparse import issparse
  from ..utils import check_array
  from ..utils import gen_even_slices
  from ..utils import gen_batches
  from ..utils.fixes import partial
  from ..utils.extmath import row_norms, safe_sparse_dot
  from ..preprocessing import normalize
  from ..externals.joblib import Parallel
  from ..externals.joblib import delayed
  from ..externals.joblib import cpu_count
  from .pairwise_fast import _chi2_kernel_fast, _sparse_manhattan
label_desc: Use image node_id 10 for calling a global function with example usage: paired_manhattan_distances(X, Y) and returns: np, np