class_docstr: Unbiased Maximum Mean Discrepancy (MMD) is a kernel-based method for measuring the similarity between two
  distributions. It is a non-negative metric where a smaller value indicates a closer match between the two
  distributions.
  
  Gretton, A., et al,, 2012.  A kernel two-sample test. The Journal of Machine Learning Research, 13(1), pp.723-773.
  
  Args:
      y_mapping: Callable to transform the y tensors before computing the metric. It is usually a Gaussian or Laplace
          filter, but it can be any function that takes a tensor as input and returns a tensor as output such as a
          feature extractor or an Identity function., e.g. `y_mapping = lambda x: x.square()`.
code: def __init__(self, y_mapping: Callable | None = None) -> None:
      super().__init__()
      self.y_mapping = y_mapping
  
file_path: MONAI/monai/metrics/mmd.py
imports: from __future__ import annotations
  from collections.abc import Callable
  import torch
  from monai.metrics.metric import Metric
label_desc: Use image node_id 1 to create a new MMDMetric object from inherited base classes: Metric with example: obj = MMDMetric(y_mapping)