class_docstr: Hamming kernel implementation.
docstr: Hyperparameter of the length scale.
docstr_tok: ["Hyperparameter","of","the","length","scale","."]
code: def hyperparameter_length_scale(self) -> kernels.Hyperparameter:
      """Hyperparameter of the length scale."""
      length_scale = self.length_scale
      anisotropic = np.iterable(length_scale) and len(length_scale) > 1  # type: ignore
  
      if anisotropic:
          return kernels.Hyperparameter(
              "length_scale",
              "numeric",
              self.length_scale_bounds,
              len(length_scale),  # type: ignore
          )
  
      return kernels.Hyperparameter(
          "length_scale",
          "numeric",
          self.length_scale_bounds,
      )
  
file_path: SMAC3/smac/model/gaussian_process/kernels/hamming_kernel.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  import sklearn.gaussian_process.kernels
  from smac.model.gaussian_process.kernels.base_kernels import AbstractKernel
  from smac.model.gaussian_process.priors.abstract_prior import AbstractPrior
label_desc: Use image node_id 3 for calling the HammingKernel obj's underlying member method code with example usage: obj.hyperparameter_length_scale() and returns: kernels, kernels