class_docstr: Mean squared error metric. Computes the pixelwise squared error which is then averaged with decorators.
  Decorated with a mean and running_mean. Default for key: 'mse'.
  
  Args:
      pred_key (StateKey): The key in state which holds the predicted values
      target_key (StateKey): The key in state which holds the target values
class_docstr_tok: ["Mean","squared","error","metric",".","Computes","the","pixelwise","squared","error","which","is","then","averaged","with","decorators",".","Decorated","with","a","mean","and","running_mean",".","Default","for","key",":","'mse","'",".","Args",":","pred_key","(","StateKey",")",":","The","key","in","state","which","holds","the","predicted","values","target_key","(","StateKey",")",":","The","key","in","state","which","holds","the","target","values"]
code: def process(self, *args):
      state = args[0]
      y_pred = state[self.pred_key]
      y_true = state[self.target_key]
      return torch.pow(y_pred - y_true.view_as(y_pred), 2).data
  
imports: import torchbearer
  from torchbearer import Metric
  from .decorators import default_for_key, running_mean, mean, std, to_dict
  import torch
label_desc: Use image node_id 2 for calling the MeanSquaredError obj's underlying member method code with example usage: obj.process() and returns: torch