class_docstr: Simply returns the 'loss' value from the model state. Decorated with a mean, running_mean and std. Default for
  key: 'loss'.
  
  State Requirements:
      - :attr:`torchbearer.state.LOSS`: This key should map to the loss for the current batch
class_docstr_tok: ["Simply","returns","the","'loss","'","value","from","the","model","state",".","Decorated","with","a","mean",",","running_mean","and","std",".","Default","for","key",":","'loss","'",".","State","Requirements",":","-",":","attr",":","`","torchbearer.state.LOSS","`",":","This","key","should","map","to","the","loss","for","the","current","batch"]
code: def __init__(self):
      super(Loss, self).__init__("loss")
  
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 Loss object from inherited base classes: Metric with example: obj = Loss()