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 progress(self):
      # TODO: Erase the previous progress text to update in place
      text = "Step {}: ".format(self.step)
      metrics = self.history[self.step]
      for k, v in metrics.items():
          # Skip timestamp
          if k == "__timestamp__":
              continue
          # Exclude lists, dicts, and arrays
          # TODO: ideally, include the skipped types with a compact representation
          if not isinstance(v, (list, dict, np.ndarray)):
              text += "{}: {}  ".format(k, v)
      print(text)
  
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 6 for calling the History obj's underlying member method code with example usage: obj.progress() without return types