class_docstr: A minimal (but valid) convolutional kernel for sequences of variable
  lengths.
code: def __init__(
      self,
      baseline_similarity=0.5,
      baseline_similarity_bounds=(1e-5, 1),
  ):
      self.baseline_similarity = baseline_similarity
      self.baseline_similarity_bounds = baseline_similarity_bounds
  
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 1 to create a new SequenceKernel object from inherited base classes: GenericKernelMixin, Kernel with example: obj = SequenceKernel(baseline_similarity, baseline_similarity_bounds)