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 __init__(
      self, pred_key=torchbearer.Y_PRED, target_key=torchbearer.Y_TRUE
  ):
      super(MeanSquaredError, self).__init__("mse")
      self.pred_key = pred_key
      self.target_key = target_key
  
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 1 to create a new MeanSquaredError object from inherited base classes: Metric with example: obj = MeanSquaredError(pred_key, target_key)