class_docstr: A minimal (but valid) convolutional kernel for sequences of variable
  length.
code: def __call__(self, X, Y=None, eval_gradient=False):
      if Y is None:
          Y = X
  
      if eval_gradient:
          return (
              np.array([[self._f(x, y) for y in Y] for x in X]),
              np.array([[[self._g(x, y)] for y in Y] for x in X]),
          )
      else:
          return np.array([[self._f(x, y) for y in Y] for x in X])
  
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 5 for calling the MiniSeqKernel obj's underlying member method code with example usage: obj.__call__(X, Y, eval_gradient) and returns: np,  np, np