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 process(self, *args):
      state = args[0]
      return state[torchbearer.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 2 for calling the Loss obj's underlying member method code with example usage: obj.process() and returns: state