class_docstr: Calculates the `mean squared error <https://en.wikipedia.org/wiki/Mean_squared_error>`_.
  
  .. math:: \text{MSE} = \frac{1}{N} \sum_{i=1}^N \|y_{i} - x_{i}\|^2
  
  where :math:`y_{i}` is the prediction tensor and :math:`x_{i}` is ground true tensor.
  
  - ``update`` must receive output of the form ``(y_pred, y)``.
  
  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 the format of
      ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y, ...}``. If not, ``output_tranform`` can be added
      to the metric to transform the output into the form expected by the metric.
  
      ``y_pred`` and ``y`` should have the same shape.
  
      For more information on how metric works with :class:`~ignite.engine.engine.Engine`, visit :ref:`attach-engine`.
  
      .. include:: defaults.rst
          :start-after: :orphan:
  
      .. testcode::
  
          metric = MeanSquaredError()
          metric.attach(default_evaluator, 'mse')
          preds = torch.tensor([
              [1, 2, 4, 1],
              [2, 3, 1, 5],
              [1, 3, 5, 1],
              [1, 5, 1 ,11]
          ])
          target = preds * 0.75
          state = default_evaluator.run([[preds, target]])
          print(state.metrics['mse'])
  
      .. testoutput::
  
          3.828125
code: def compute(self) -> Union[float, torch.Tensor]:
      if self._num_examples == 0:
          raise NotComputableError(
              "MeanSquaredError must have at least one example before it can be computed."
          )
      return self._sum_of_squared_errors.item() / self._num_examples
  
file_path: ignite/ignite/metrics/mean_squared_error.py
imports: from typing import Sequence, Union
  import torch
  from ignite.exceptions import NotComputableError
  from ignite.metrics.metric import Metric, reinit__is_reduced, sync_all_reduce
label_desc: Use image node_id 3 for calling the MeanSquaredError obj's underlying member method code with example usage: obj.compute() and returns: unknown