class_docstr: A minimal (but valid) convolutional kernel for sequences of variable
  lengths.
docstr: kernel value between a pair of sequences
docstr_tok: ["kernel","value","between","a","pair","of","sequences"]
code: def _f(self, s1, s2):
      """
      kernel value between a pair of sequences
      """
      return sum(
          [
              1.0 if c1 == c2 else self.baseline_similarity
              for c1 in s1
              for c2 in s2
          ]
      )
  
file_path: scikit-learn/examples/gaussian_process/plot_gpr_on_structured_data.py
imports: import numpy
  from sklearn.base import clone
  from sklearn.gaussian_process import GaussianProcessClassifier, GaussianProcessRegressor
  from sklearn.gaussian_process.kernels import GenericKernelMixin, Hyperparameter, Kernel
  import matplotlib.pyplot
label_desc: Use image node_id 3 for calling the SequenceKernel obj's underlying member method code with example usage: obj._f(s1, s2) and returns: sum