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","."]
code: def load(self, file_name):
      with open(file_name, "rb") as f:
          self.history = pickle.load(f)
      # Set last step and metrics
      self.step = self.steps[-1]
      unique_metrics = set(
          itertools.chain(*[m.keys() for m in self.history.values()])
      )
      self.metrics = unique_metrics - {
          "__timestamp__",
      }
  
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 10 for calling the History obj's underlying member method code with example usage: obj.load(file_name) without return types