class_docstr: Drift detector based on the kernel Maximum Mean Discrepancy (MMD) test.
  
  This is modelled after the MMD drift detection in
  S. Rabanser et al: *Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift* (NeurIPS), 2019.
  
  Note that our heuristic choice of the kernel bandwith is more closely aligned with that of the original MMD paper and code than S. Rabanser's.
  
  The default kernel is the unnormalized Gaussian (or Squared Exponential) kernel.
code: def __init__(
      self,
      *,
      return_p_value=False,
      n_perm: int = 1000,
      kernel=GaussianKernel(),
  ):
      super().__init__(return_p_value=return_p_value)
      self.n_perm = n_perm
      self.kernel = kernel
      self.n_test = None
      self.scores = None
  
imports: from typing import Optional
  import torch
  from .None import Detector
  import torchdrift.utils
label_desc: Use image node_id 1 to create a new KernelMMDDriftDetector object from inherited base classes: Detector with example: obj = KernelMMDDriftDetector()