class_docstr: Calculates the Canberra Metric.
  
  .. math::
      \text{CM} = \sum_{j=1}^n\frac{|A_j - P_j|}{|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`_ or `scikit-learn distance metrics`_
  
  - ``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)`.
  
  .. _scikit-learn distance metrics:
      https://scikit-learn.org/stable/modules/generated/sklearn.metrics.DistanceMetric.html
  
  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.
  
  .. _`Botchkarev 2018`:
      https://arxiv.org/ftp/arxiv/papers/1809/1809.03006.pdf
  
  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 = CanberraMetric()
              metric.attach(default_evaluator, 'canberra')
              y_pred = torch.tensor([[3.8], [9.9], [-5.4], [2.1]])
              y_true = y_pred * 1.5
              state = default_evaluator.run([[y_pred, y_true]])
              print(state.metrics['canberra'])
  
      .. testoutput::
  
              0.8000...
  
  .. versionchanged:: 0.4.3
  
      - Fixed implementation: ``abs`` in denominator.
      - Works with DDP.
code: def _update(self, output: Tuple[torch.Tensor, torch.Tensor]) -> None:
      y_pred, y = output[0].detach(), output[1].detach()
      errors = torch.abs(y - y_pred) / (
          torch.abs(y_pred) + torch.abs(y) + 1e-15
      )
      self._sum_of_errors += torch.sum(errors).to(self._device)
  
file_path: ignite/ignite/contrib/metrics/regression/canberra_metric.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 2 for calling the CanberraMetric obj's underlying member method code with example usage: obj._update(output) without return types