class_docstr: Tracks training progress and visualizes it.
  For example, use it to track the training and validation loss and accuracy
  and plot them.
class_docstr_tok: ["Tracks","training","progress","and","visualizes","it",".","For","example",",","use","it","to","track","the","training","and","validation","loss","and","accuracy","and","plot","them","."]
docstr: Returns the total period between when the first and last steps
  where logged. This usually correspnods to the total training time
  if there were no gaps in the training.
docstr_tok: ["Returns","the","total","period","between","when","the","first","and","last","steps","where","logged",".","This","usually","correspnods","to","the","total","training","time","if","there","were","no","gaps","in","the","training","."]
code: def get_total_time(self):
      """Returns the total period between when the first and last steps
      where logged. This usually correspnods to the total training time
      if there were no gaps in the training.
      """
      first_step = self.steps[0]
      last_step = self.steps[-1]
      seconds = (
          self.history[last_step]["__timestamp__"]
          - self.history[first_step]["__timestamp__"]
      )
      return datetime.timedelta(seconds=seconds)
  
imports: import math
  import random
  import io
  import itertools
  import time
  import datetime
  import pickle
  import numpy
  import matplotlib.pyplot
  import matplotlib
  from .None import utils
label_desc: Use image node_id 8 for calling the History obj's underlying member method code with example usage: obj.get_total_time() and returns: datetime