class_docstr: History of metrics.
  
  History contains the metrics recorded during training and evaluation.
  Save data with history.append and get a sequence of data by calling
  history.get.
  
  For example:
  history.append('train', 'metrics/accuracy', 1, 0.04)
  history.append('train', 'metrics/accuracy', 1000, 0.31)
  history.get('train', 'metrics/accuracy')
  # returns [(1, 0.04), (1000, 0.31)]
class_docstr_tok: ["History","of","metrics",".","History","contains","the","metrics","recorded","during","training","and","evaluation",".","Save","data","with","history.append","and","get","a","sequence","of","data","by","calling","history.get",".","For","example",":","history.append","(","'train","'",",","'metrics\/accuracy","'",",","1",",","0.04",")","history.append","(","'train","'",",","'metrics\/accuracy","'",",","1000",",","0.31",")","history.get","(","'train","'",",","'metrics\/accuracy","'",")","#","returns","[","(","1",",","0.04",")",",","(","1000",",","0.31",")","]"]
docstr: Constructs a `History` from a Python dictionary of parameters.
docstr_tok: ["Constructs","a","`","History","`","from","a","Python","dictionary","of","parameters","."]
code: def from_dict(cls, json_object):
      """Constructs a `History` from a Python dictionary of parameters."""
      history = History()
      for key, value in six.iteritems(json_object):
          history.__dict__[key] = value
      return history
  
file_path: trax/trax/supervised/history.py
imports: import collections
  import copy
  from absl import logging
  import six
label_desc: Use image node_id 2 for calling the History obj's underlying member method code with example usage: obj.from_dict(cls, json_object) and returns: history