class_docstr: Hamming kernel implementation.
code: def meta(self) -> dict[str, Any]:  # noqa: D102
      meta = super().meta
  
      length_scale = self.length_scale
      if isinstance(length_scale, np.ndarray):
          length_scale = length_scale.tolist()
  
      length_scale_bounds = self.length_scale_bounds
      if isinstance(length_scale_bounds, np.ndarray):
          length_scale_bounds = length_scale_bounds.tolist()
  
      meta.update(
          {
              "length_scale": length_scale,
              "lengthscale_bounds": length_scale_bounds,
          }
      )
  
      return meta
  
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 2 for calling the HammingKernel obj's underlying member method code with example usage: obj.meta() and returns: meta