code: def laplacian_kernel(X, Y=None, gamma=None):
      """Compute the laplacian kernel between X and Y.
  
      The laplacian kernel is defined as::
  
          K(x, y) = exp(-gamma ||x-y||_1)
  
      for each pair of rows x in X and y in Y.
      Read more in the :ref:`User Guide <laplacian_kernel>`.
  
      .. versionadded:: 0.17
  
      Parameters
      ----------
      X : array of shape (n_samples_X, n_features)
  
      Y : array of shape (n_samples_Y, n_features)
  
      gamma : float, default None
          If None, defaults to 1.0 / n_samples_X
  
      Returns
      -------
      kernel_matrix : array of shape (n_samples_X, n_samples_Y)
      """
      X, Y = check_pairwise_arrays(X, Y)
      if gamma is None:
          gamma = 1.0 / X.shape[1]
  
      K = -gamma * manhattan_distances(X, Y)
      np.exp(K, K)  # exponentiate K in-place
      return K
  
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 17 for calling a global function with example usage: laplacian_kernel(X, Y, gamma) and returns: K