class_docstr: A minimal (but valid) convolutional kernel for sequences of variable
  length.
code: def _g(self, s1, s2):
      return sum([0.0 if c1 == c2 else 1.0 for c1 in s1 for c2 in s2])
  
file_path: scikit-learn/sklearn/gaussian_process/tests/_mini_sequence_kernel.py
imports: import numpy
  from sklearn.base import clone
  from sklearn.gaussian_process.kernels import GenericKernelMixin, Hyperparameter, Kernel, StationaryKernelMixin
label_desc: Use image node_id 4 for calling the MiniSeqKernel obj's underlying member method code with example usage: obj._g(s1, s2) and returns: sum