class_docstr: Calculates the Wave Hedges Distance.
  
  .. math::
      \text{WHD} = \sum_{j=1}^n\frac{|A_j - P_j|}{max(A_j, P_j)}
  
  where, :math:`A_j` is the ground truth and :math:`P_j` is the predicted value.
  
  More details can be found in `Botchkarev 2018`__.
  
  - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
  - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)`.
  
  __ https://arxiv.org/abs/1809.03006
  
  Parameters are inherited from ``Metric.__init__``.
  
  Args:
      output_transform: a callable that is used to transform the
          :class:`~ignite.engine.engine.Engine`'s ``process_function``'s output into the
          form expected by the metric. This can be useful if, for example, you have a multi-output model and
          you want to compute the metric with respect to one of the outputs.
          By default, metrics require the output as ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
      device: specifies which device updates are accumulated on. Setting the
          metric's device to be the same as your ``update`` arguments ensures the ``update`` method is
          non-blocking. By default, CPU.
  
  Examples:
      To use with ``Engine`` and ``process_function``, simply attach the metric instance to the engine.
      The output of the engine's ``process_function`` needs to be in format of
      ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``.
  
      .. include:: defaults.rst
          :start-after: :orphan:
  
      .. testcode::
  
          metric = WaveHedgesDistance()
          metric.attach(default_evaluator, 'whd')
          y_true = torch.tensor([0., 1., 2., 3., 4., 5.])
          y_pred = y_true * 0.75
          state = default_evaluator.run([[y_pred, y_true]])
          print(state.metrics['whd'])
  
      .. testoutput::
  
          1.25...
  
  .. versionchanged:: 0.4.5
      - Works with DDP.
code: def reset(self) -> None:
      self._sum_of_errors = torch.tensor(0.0, device=self._device)
  
file_path: ignite/ignite/contrib/metrics/regression/wave_hedges_distance.py
imports: from typing import Tuple
  import torch
  from ignite.contrib.metrics.regression._base import _BaseRegression
  from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
label_desc: Use image node_id 1 for calling the WaveHedgesDistance obj's underlying member method code with example usage: obj.reset() without return types