class_docstr: Computes the mean square error between labels and predictions.
  
  `loss = square(abs(y_true - y_pred), axis=-1)`
  
  Usage:
  
  ```python
  pred = torch.tensor([[1, -2], [1, 1]])
  target = torch.tensor([[0, 1], [0, 1]])
  m = MSE()
  m(pred, target)
  print(m.compute())  # tensor(2.7500)
  ```
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code: def __init__(self):
      self.total = torch.tensor(0)
      self.sum_squared_error = torch.tensor(0.0)
  
imports: import torch
  from abc import ABC, abstractmethod
label_desc: Use image node_id 1 to create a new MSE object from inherited base classes: PytorchMetric with example: obj = MSE()