code: def kernel_metrics():
      """Valid metrics for pairwise_kernels
  
      This function simply returns the valid pairwise distance metrics.
      It exists, however, to allow for a verbose description of the mapping for
      each of the valid strings.
  
      The valid distance metrics, and the function they map to, are:
        ===============   ========================================
        metric            Function
        ===============   ========================================
        'additive_chi2'   sklearn.pairwise.additive_chi2_kernel
        'chi2'            sklearn.pairwise.chi2_kernel
        'linear'          sklearn.pairwise.linear_kernel
        'poly'            sklearn.pairwise.polynomial_kernel
        'polynomial'      sklearn.pairwise.polynomial_kernel
        'rbf'             sklearn.pairwise.rbf_kernel
        'laplacian'       sklearn.pairwise.laplacian_kernel
        'sigmoid'         sklearn.pairwise.sigmoid_kernel
        'cosine'          sklearn.pairwise.cosine_similarity
        ===============   ========================================
  
      Read more in the :ref:`User Guide <metrics>`.
      """
      return PAIRWISE_KERNEL_FUNCTIONS
  
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 25 for calling a global function with example usage: kernel_metrics() and returns: PAIRWISE_KERNEL_FUNCTIONS