class_docstr: Compute `mean squared error`_ (MSE).
  
  .. math:: \text{MSE} = \frac{1}{N}\sum_i^N(y_i - \hat{y_i})^2
  
  Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.
  
  As input to ``forward`` and ``update`` the metric accepts the following input:
  
  - ``preds`` (:class:`~torch.Tensor`): Predictions from model
  - ``target`` (:class:`~torch.Tensor`): Ground truth values
  
  As output of ``forward`` and ``compute`` the metric returns the following output:
  
  - ``mean_squared_error`` (:class:`~torch.Tensor`): A tensor with the mean squared error
  
  Args:
      squared: If True returns MSE value, if False returns RMSE value.
      num_outputs: Number of outputs in multioutput setting
      kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
  
  Example::
      Single output mse computation:
  
      >>> from torch import tensor
      >>> from torchmetrics.regression import MeanSquaredError
      >>> target = tensor([2.5, 5.0, 4.0, 8.0])
      >>> preds = tensor([3.0, 5.0, 2.5, 7.0])
      >>> mean_squared_error = MeanSquaredError()
      >>> mean_squared_error(preds, target)
      tensor(0.8750)
  
  Example::
      Multioutput mse computation:
  
      >>> from torch import tensor
      >>> from torchmetrics.regression import MeanSquaredError
      >>> target = tensor([[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]])
      >>> preds = tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
      >>> mean_squared_error = MeanSquaredError(num_outputs=3)
      >>> mean_squared_error(preds, target)
      tensor([1., 4., 9.])
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code: def __init__(
      self,
      squared: bool = True,
      num_outputs: int = 1,
      **kwargs: Any,
  ) -> None:
      super().__init__(**kwargs)
  
      if not isinstance(squared, bool):
          raise ValueError(
              f"Expected argument `squared` to be a boolean but got {squared}"
          )
      self.squared = squared
  
      if not (isinstance(num_outputs, int) and num_outputs > 0):
          raise ValueError(
              f"Expected num_outputs to be a positive integer but got {num_outputs}"
          )
      self.num_outputs = num_outputs
  
      self.add_state(
          "sum_squared_error",
          default=torch.zeros(num_outputs),
          dist_reduce_fx="sum",
      )
      self.add_state("total", default=tensor(0), dist_reduce_fx="sum")
  
file_path: torchmetrics/src/torchmetrics/regression/mse.py
imports: from typing import Any, Optional, Sequence, Union
  import torch
  from torch import Tensor, tensor
  from torchmetrics.functional.regression.mse import _mean_squared_error_compute, _mean_squared_error_update
  from torchmetrics.metric import Metric
  from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE
  from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
label_desc: Use image node_id 1 to create a new MeanSquaredError object from inherited base classes: Metric with example: obj = MeanSquaredError(squared, num_outputs)